diff --git a/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp b/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp index 5854cd3d83..63578469ea 100644 --- a/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp +++ b/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp @@ -1,1767 +1,1751 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkImageStatisticsCalculator.h" #include #include #include #include #include #include #include #include #include #include #include /** * \brief Test class for mitkImageStatisticsCalculator * * This test covers: * - instantiation of an ImageStatisticsCalculator class * - correctness of statistics when using PlanarFigures for masking */ class mitkImageStatisticsCalculatorTestSuite : public mitk::TestFixture { CPPUNIT_TEST_SUITE(mitkImageStatisticsCalculatorTestSuite); MITK_TEST(TestUninitializedImage); MITK_TEST(TestCase1); MITK_TEST(TestCase2); MITK_TEST(TestCase3); MITK_TEST(TestCase4); MITK_TEST(TestCase5); MITK_TEST(TestCase6); MITK_TEST(TestCase7); MITK_TEST(TestCase8); MITK_TEST(TestCase9); MITK_TEST(TestCase10); MITK_TEST(TestCase11); MITK_TEST(TestCase12); MITK_TEST(TestImageMaskingEmpty); MITK_TEST(TestImageMaskingNonEmpty); MITK_TEST(TestRecomputeOnModifiedMask); MITK_TEST(TestPic3DStatistics); MITK_TEST(TestPic3DAxialPlanarFigureMaskStatistics); MITK_TEST(TestPic3DSagittalPlanarFigureMaskStatistics); MITK_TEST(TestPic3DCoronalPlanarFigureMaskStatistics); MITK_TEST(TestPic3DImageMaskStatistics_label1); MITK_TEST(TestPic3DImageMaskStatistics_label2); MITK_TEST(TestPic3DIgnorePixelValueMaskStatistics); MITK_TEST(TestPic3DSecondaryMaskStatistics); MITK_TEST(TestUS4DCylStatistics_time1); MITK_TEST(TestUS4DCylAxialPlanarFigureMaskStatistics_time1); MITK_TEST(TestUS4DCylSagittalPlanarFigureMaskStatistics_time1); MITK_TEST(TestUS4DCylCoronalPlanarFigureMaskStatistics_time1); MITK_TEST(TestUS4DCylImageMaskStatistics_time1_label_1); MITK_TEST(TestUS4DCylImageMaskStatistics_time2_label_1); MITK_TEST(TestUS4DCylImageMaskStatistics_time1_label_2); MITK_TEST(TestUS4DCylIgnorePixelValueMaskStatistics_time1); MITK_TEST(TestUS4DCylSecondaryMaskStatistics_time1); CPPUNIT_TEST_SUITE_END(); public: void setUp() override; void tearDown() override; void TestUninitializedImage(); void TestCase1(); void TestCase2(); void TestCase3(); void TestCase4(); void TestCase5(); void TestCase6(); void TestCase7(); void TestCase8(); void TestCase9(); void TestCase10(); void TestCase11(); void TestCase12(); void TestImageMaskingEmpty(); void TestImageMaskingNonEmpty(); void TestRecomputeOnModifiedMask(); void TestPic3DStatistics(); void TestPic3DAxialPlanarFigureMaskStatistics(); void TestPic3DSagittalPlanarFigureMaskStatistics(); void TestPic3DCoronalPlanarFigureMaskStatistics(); void TestPic3DImageMaskStatistics_label1(); void TestPic3DImageMaskStatistics_label2(); void TestPic3DIgnorePixelValueMaskStatistics(); void TestPic3DSecondaryMaskStatistics(); void TestUS4DCylStatistics_time1(); void TestUS4DCylAxialPlanarFigureMaskStatistics_time1(); void TestUS4DCylSagittalPlanarFigureMaskStatistics_time1(); void TestUS4DCylCoronalPlanarFigureMaskStatistics_time1(); void TestUS4DCylImageMaskStatistics_time1_label_1(); void TestUS4DCylImageMaskStatistics_time2_label_1(); void TestUS4DCylImageMaskStatistics_time1_label_2(); void TestUS4DCylIgnorePixelValueMaskStatistics_time1(); void TestUS4DCylSecondaryMaskStatistics_time1(); void TestDifferentNBinsForHistogramStatistics(); void TestDifferentBinSizeForHistogramStatistic(); void TestSwitchFromBinSizeToNBins(); void TestSwitchFromNBinsToBinSize(); private: mitk::Image::Pointer m_TestImage; mitk::Image::Pointer m_Pic3DImage; mitk::Image::Pointer m_Pic3DImageMask; mitk::Image::Pointer m_Pic3DImageMask2; mitk::PlanarFigure::Pointer m_Pic3DPlanarFigureAxial; mitk::PlanarFigure::Pointer m_Pic3DPlanarFigureSagittal; mitk::PlanarFigure::Pointer m_Pic3DPlanarFigureCoronal; mitk::Image::Pointer m_US4DImage; mitk::Image::Pointer m_US4DImageMask; mitk::Image::Pointer m_US4DImageMask2; mitk::PlanarFigure::Pointer m_US4DPlanarFigureAxial; mitk::PlanarFigure::Pointer m_US4DPlanarFigureSagittal; mitk::PlanarFigure::Pointer m_US4DPlanarFigureCoronal; mitk::PlaneGeometry::Pointer m_Geometry; // calculate statistics for the given image and planarpolygon - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer ComputeStatistics( mitk::Image::Pointer image, + const mitk::StatisticsContainer::Pointer ComputeStatistics( mitk::Image::Pointer image, mitk::PlanarFigure::Pointer polygon ); // calculate statistics for the given image and mask - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer ComputeStatistics( mitk::Image::Pointer image, + const mitk::StatisticsContainer::Pointer ComputeStatistics( mitk::Image::Pointer image, mitk::Image::Pointer image_mask ); // universal function to calculate statistics - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer ComputeStatisticsNew(mitk::Image::Pointer image, + const mitk::StatisticsContainer::Pointer ComputeStatisticsNew(mitk::Image::Pointer image, int timeStep=0, mitk::MaskGenerator::Pointer maskGen=nullptr, mitk::MaskGenerator::Pointer secondardMaskGen=nullptr, unsigned short label=1); - void VerifyStatistics(mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats, + void VerifyStatistics(mitk::StatisticsContainer::Pointer stats, double testMean, double testSD, double testMedian=0); - void VerifyStatistics(mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats, + void VerifyStatistics(mitk::StatisticsContainer::Pointer stats, long N, double mean, double MPP, double median, double skewness, double kurtosis, double uniformity, double UPP, double variance, double stdev, double min, double max, double RMS, double entropy, vnl_vector minIndex, vnl_vector maxIndex); }; void mitkImageStatisticsCalculatorTestSuite::tearDown() { m_TestImage = nullptr; m_Pic3DImage = nullptr; m_Pic3DImageMask = nullptr; m_Pic3DImageMask2 = nullptr; m_Pic3DPlanarFigureAxial = nullptr; m_Pic3DPlanarFigureSagittal = nullptr; m_Pic3DPlanarFigureCoronal = nullptr; m_US4DImage = nullptr; m_US4DImageMask = nullptr; m_US4DImageMask2 = nullptr; m_US4DPlanarFigureAxial = nullptr; m_US4DPlanarFigureSagittal = nullptr; m_US4DPlanarFigureCoronal = nullptr; m_Geometry = nullptr; } void mitkImageStatisticsCalculatorTestSuite::setUp() { std::string filename = this->GetTestDataFilePath("ImageStatisticsTestData/testimage.dcm"); std::string Pic3DFile = this->GetTestDataFilePath("Pic3D.nrrd"); std::string Pic3DImageMaskFile = this->GetTestDataFilePath("ImageStatisticsTestData/Pic3D-labels.nrrd"); std::string Pic3DImageMaskFile2 = this->GetTestDataFilePath("ImageStatisticsTestData/Pic3D-labels2.nrrd"); std::string Pic3DAxialPlanarFigureFile = this->GetTestDataFilePath("ImageStatisticsTestData/Pic3DAxialPlanarFigure.pf"); std::string Pic3DSagittalPlanarFigureFile = this->GetTestDataFilePath("ImageStatisticsTestData/Pic3DSagittalPlanarFigure.pf"); std::string Pic3DCoronalPlanarFigureFile = this->GetTestDataFilePath("ImageStatisticsTestData/Pic3DCoronalPlanarFigure.pf"); std::string US4DFile = this->GetTestDataFilePath("US4DCyl.nrrd"); std::string US4DImageMaskFile = this->GetTestDataFilePath("ImageStatisticsTestData/US4D-labels.nrrd"); std::string US4DImageMaskFile2 = this->GetTestDataFilePath("ImageStatisticsTestData/US4D-labels2.nrrd"); std::string US4DAxialPlanarFigureFile = this->GetTestDataFilePath("ImageStatisticsTestData/US4DAxialPlanarFigure.pf"); std::string US4DSagittalPlanarFigureFile = this->GetTestDataFilePath("ImageStatisticsTestData/US4DSagittalPlanarFigure.pf"); std::string US4DCoronalPlanarFigureFile = this->GetTestDataFilePath("ImageStatisticsTestData/US4DCoronalPlanarFigure.pf"); if (filename.empty() || Pic3DFile.empty() || Pic3DImageMaskFile.empty() || Pic3DAxialPlanarFigureFile.empty() || Pic3DSagittalPlanarFigureFile.empty() || Pic3DCoronalPlanarFigureFile.empty() || US4DFile.empty() || US4DImageMaskFile.empty() || US4DAxialPlanarFigureFile.empty() || US4DSagittalPlanarFigureFile.empty() || US4DCoronalPlanarFigureFile.empty()) { MITK_TEST_FAILED_MSG( << "Could not find test file" ) } MITK_TEST_OUTPUT(<< "Loading test image '" << filename << "'") m_TestImage = dynamic_cast(mitk::IOUtil::Load(filename)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_TestImage.IsNotNull(), "Loaded an mitk::Image" ); m_Geometry = m_TestImage->GetSlicedGeometry()->GetPlaneGeometry(0); MITK_TEST_CONDITION_REQUIRED( m_Geometry.IsNotNull(), "Getting image geometry" ); m_Pic3DImage = dynamic_cast(mitk::IOUtil::Load(Pic3DFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_Pic3DImage.IsNotNull(), "Loaded Pic3D" ); m_Pic3DImageMask = dynamic_cast(mitk::IOUtil::Load(Pic3DImageMaskFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_Pic3DImageMask.IsNotNull(), "Loaded Pic3D image mask" ); m_Pic3DImageMask2 = dynamic_cast(mitk::IOUtil::Load(Pic3DImageMaskFile2)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_Pic3DImageMask2.IsNotNull(), "Loaded Pic3D image secondary mask" ); m_Pic3DPlanarFigureAxial = dynamic_cast(mitk::IOUtil::Load(Pic3DAxialPlanarFigureFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_Pic3DPlanarFigureAxial.IsNotNull(), "Loaded Pic3D axial planarFigure" ); m_Pic3DPlanarFigureSagittal = dynamic_cast(mitk::IOUtil::Load(Pic3DSagittalPlanarFigureFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_Pic3DPlanarFigureSagittal.IsNotNull(), "Loaded Pic3D sagittal planarFigure" ); m_Pic3DPlanarFigureCoronal = dynamic_cast(mitk::IOUtil::Load(Pic3DCoronalPlanarFigureFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_Pic3DPlanarFigureCoronal.IsNotNull(), "Loaded Pic3D coronal planarFigure" ); m_US4DImage = dynamic_cast(mitk::IOUtil::Load(US4DFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_US4DImage.IsNotNull(), "Loaded US4D" ); m_US4DImageMask = dynamic_cast(mitk::IOUtil::Load(US4DImageMaskFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_US4DImageMask.IsNotNull(), "Loaded US4D image mask" ); m_US4DImageMask2 = dynamic_cast(mitk::IOUtil::Load(US4DImageMaskFile2)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_US4DImageMask2.IsNotNull(), "Loaded US4D image mask2" ); m_US4DPlanarFigureAxial = dynamic_cast(mitk::IOUtil::Load(US4DAxialPlanarFigureFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_US4DPlanarFigureAxial.IsNotNull(), "Loaded US4D axial planarFigure" ); m_US4DPlanarFigureSagittal = dynamic_cast(mitk::IOUtil::Load(US4DSagittalPlanarFigureFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_US4DPlanarFigureSagittal.IsNotNull(), "Loaded US4D sagittal planarFigure" ); m_US4DPlanarFigureCoronal = dynamic_cast(mitk::IOUtil::Load(US4DCoronalPlanarFigureFile)[0].GetPointer()); MITK_TEST_CONDITION_REQUIRED( m_US4DPlanarFigureCoronal.IsNotNull(), "Loaded US4D coronal planarFigure" ); } void mitkImageStatisticsCalculatorTestSuite::TestCase1() { /***************************** * one whole white pixel * -> mean of 255 expected ******************************/ MITK_INFO << std::endl << "Test case 1:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 10.5 ; pnt1[1] = 3.5; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.5; pnt2[1] = 3.5; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 9.5; pnt3[1] = 4.5; figure1->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 10.5; pnt4[1] = 4.5; figure1->SetControlPoint( 3, pnt4, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 255.0, 0.0, 255.0); } void mitkImageStatisticsCalculatorTestSuite::TestCase2() { /***************************** * half pixel in x-direction (white) * -> mean of 255 expected ******************************/ MITK_INFO << std::endl << "Test case 2:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 10.0 ; pnt1[1] = 3.5; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.5; pnt2[1] = 3.5; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 9.5; pnt3[1] = 4.5; figure1->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 10.0; pnt4[1] = 4.5; figure1->SetControlPoint( 3, pnt4, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 255.0, 0.0, 255.0); } void mitkImageStatisticsCalculatorTestSuite::TestCase3() { /***************************** * half pixel in diagonal-direction (white) * -> mean of 255 expected ******************************/ MITK_INFO << std::endl << "Test case 3:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 10.5 ; pnt1[1] = 3.5; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.5; pnt2[1] = 3.5; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 9.5; pnt3[1] = 4.5; figure1->SetControlPoint( 2, pnt3, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 255.0, 0.0, 255.0); } void mitkImageStatisticsCalculatorTestSuite::TestCase4() { /***************************** * one pixel (white) + 2 half pixels (white) + 1 half pixel (black) * -> mean of 191.25 expected ******************************/ MITK_INFO << std::endl << "Test case 4:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 1.1; pnt1[1] = 1.1; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 2.0; pnt2[1] = 2.0; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 3.0; pnt3[1] = 1.0; figure1->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 2.0; pnt4[1] = 0.0; figure1->SetControlPoint( 3, pnt4, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 191.25, 110.41, 242.250); } void mitkImageStatisticsCalculatorTestSuite::TestCase5() { /***************************** * whole pixel (white) + half pixel (gray) in x-direction * -> mean of 191.5 expected ******************************/ MITK_INFO << std::endl << "Test case 5:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 11.0; pnt1[1] = 3.5; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.5; pnt2[1] = 3.5; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 9.5; pnt3[1] = 4.5; figure1->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 11.0; pnt4[1] = 4.5; figure1->SetControlPoint( 3, pnt4, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 191.50, 63.50, 134.340); } void mitkImageStatisticsCalculatorTestSuite::TestCase6() { /***************************** * quarter pixel (black) + whole pixel (white) + half pixel (gray) in x-direction * -> mean of 191.5 expected ******************************/ MITK_INFO << std::endl << "Test case 6:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 11.0; pnt1[1] = 3.5; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.25; pnt2[1] = 3.5; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 9.25; pnt3[1] = 4.5; figure1->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 11.0; pnt4[1] = 4.5; figure1->SetControlPoint( 3, pnt4, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 191.5, 63.50, 134.340); } void mitkImageStatisticsCalculatorTestSuite::TestCase7() { /***************************** * half pixel (black) + whole pixel (white) + half pixel (gray) in x-direction * -> mean of 127.66 expected ******************************/ MITK_INFO << std::endl << "Test case 7:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure1 = mitk::PlanarPolygon::New(); figure1->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 11.0; pnt1[1] = 3.5; figure1->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.0; pnt2[1] = 3.5; figure1->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 9.0; pnt3[1] = 4.0; figure1->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 11.0; pnt4[1] = 4.0; figure1->SetControlPoint( 3, pnt4, true ); figure1->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure1.GetPointer()), 127.66, 104.1, 140.250); } void mitkImageStatisticsCalculatorTestSuite::TestCase8() { /***************************** * whole pixel (gray) * -> mean of 128 expected ******************************/ MITK_INFO << std::endl << "Test case 8:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure2 = mitk::PlanarPolygon::New(); figure2->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 11.5; pnt1[1] = 10.5; figure2->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 11.5; pnt2[1] = 11.5; figure2->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 12.5; pnt3[1] = 11.5; figure2->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 12.5; pnt4[1] = 10.5; figure2->SetControlPoint( 3, pnt4, true ); figure2->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure2.GetPointer()), 128.0, 0.0, 128.0); } void mitkImageStatisticsCalculatorTestSuite::TestCase9() { /***************************** * whole pixel (gray) + half pixel (white) in y-direction * -> mean of 191.5 expected ******************************/ MITK_INFO << std::endl << "Test case 9:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure2 = mitk::PlanarPolygon::New(); figure2->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 11.5; pnt1[1] = 10.5; figure2->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 11.5; pnt2[1] = 12.0; figure2->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 12.5; pnt3[1] = 12.0; figure2->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 12.5; pnt4[1] = 10.5; figure2->SetControlPoint( 3, pnt4, true ); figure2->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure2.GetPointer()), 191.5, 63.50, 134.340); } void mitkImageStatisticsCalculatorTestSuite::TestCase10() { /***************************** * 2 whole pixel (white) + 2 whole pixel (black) in y-direction * -> mean of 127.66 expected ******************************/ MITK_INFO << std::endl << "Test case 10:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure2 = mitk::PlanarPolygon::New(); figure2->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 11.5; pnt1[1] = 10.5; figure2->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 11.5; pnt2[1] = 13.5; figure2->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 12.5; pnt3[1] = 13.5; figure2->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 12.5; pnt4[1] = 10.5; figure2->SetControlPoint( 3, pnt4, true ); figure2->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure2.GetPointer()), 127.66, 104.1, 140.250); } void mitkImageStatisticsCalculatorTestSuite::TestCase11() { /***************************** * 9 whole pixels (white) + 3 half pixels (white) * + 3 whole pixel (black) [ + 3 slightly less than half pixels (black)] * -> mean of 204.0 expected ******************************/ MITK_INFO << std::endl << "Test case 11:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure2 = mitk::PlanarPolygon::New(); figure2->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 0.5; pnt1[1] = 0.5; figure2->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 3.5; pnt2[1] = 3.5; figure2->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 8.4999; pnt3[1] = 3.5; figure2->SetControlPoint( 2, pnt3, true ); mitk::Point2D pnt4; pnt4[0] = 5.4999; pnt4[1] = 0.5; figure2->SetControlPoint( 3, pnt4, true ); figure2->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure2.GetPointer()), 204.0, 102.00, 242.250); } void mitkImageStatisticsCalculatorTestSuite::TestCase12() { /***************************** * half pixel (white) + whole pixel (white) + half pixel (black) * -> mean of 212.66 expected ******************************/ MITK_INFO << std::endl << "Test case 12:-----------------------------------------------------------------------------------"; mitk::PlanarPolygon::Pointer figure2 = mitk::PlanarPolygon::New(); figure2->SetPlaneGeometry( m_Geometry ); mitk::Point2D pnt1; pnt1[0] = 9.5; pnt1[1] = 0.5; figure2->PlaceFigure( pnt1 ); mitk::Point2D pnt2; pnt2[0] = 9.5; pnt2[1] = 2.5; figure2->SetControlPoint( 1, pnt2, true ); mitk::Point2D pnt3; pnt3[0] = 11.5; pnt3[1] = 2.5; figure2->SetControlPoint( 2, pnt3, true ); figure2->GetPolyLine(0); this->VerifyStatistics(ComputeStatistics(m_TestImage, figure2.GetPointer()), 212.66, 59.860, 248.640); } void mitkImageStatisticsCalculatorTestSuite::TestImageMaskingEmpty() { MITK_INFO << std::endl << "TestImageMaskingEmpty:-----------------------------------------------------------------------------------"; mitk::Image::Pointer mask_image = mitk::ImageGenerator::GenerateImageFromReference( m_TestImage, 0 ); this->VerifyStatistics( ComputeStatistics( m_TestImage, mask_image ), -21474836.480, -21474836.480, -21474836.480); // empty statisticsContainer (default values) } void mitkImageStatisticsCalculatorTestSuite::TestImageMaskingNonEmpty() { MITK_INFO << std::endl << "TestImageMaskingNonEmpty:-----------------------------------------------------------------------------------"; mitk::Image::Pointer mask_image = mitk::ImageGenerator::GenerateImageFromReference( m_TestImage, 0 ); // activate voxel in the mask image if (mask_image->GetDimension() == 3) { std::vector< itk::Index<3U> > activated_indices; itk::Index<3U> index = { { 10, 8, 0 } }; activated_indices.push_back(index); index[0] = 9; index[1] = 8; index[2] = 0; activated_indices.push_back(index); index[0] = 9; index[1] = 7; index[2] = 0; activated_indices.push_back(index); index[0] = 10; index[1] = 7; index[2] = 0; activated_indices.push_back(index); std::vector< itk::Index<3U> >::const_iterator indexIter = activated_indices.begin(); mitk::ImagePixelWriteAccessor< unsigned char, 3> writeAccess(mask_image); while (indexIter != activated_indices.end()) { writeAccess.SetPixelByIndex((*indexIter++), 1); } } if (mask_image->GetDimension() == 4) { std::vector< itk::Index<4U> > activated_indices; itk::Index<4U> index = { { 10, 8, 0, 0 } }; activated_indices.push_back(index); index[0] = 9; index[1] = 8; index[2] = 0; index[3] = 0; activated_indices.push_back(index); index[0] = 9; index[1] = 7; index[2] = 0; index[3] = 0; activated_indices.push_back(index); index[0] = 10; index[1] = 7; index[2] = 0; index[3] = 0; activated_indices.push_back(index); std::vector< itk::Index<4U> >::const_iterator indexIter = activated_indices.begin(); mitk::ImagePixelWriteAccessor< unsigned char, 4> writeAccess(mask_image); while (indexIter != activated_indices.end()) { writeAccess.SetPixelByIndex((*indexIter++), 1); } } this->VerifyStatistics( ComputeStatistics( m_TestImage, mask_image ), 127.5, 127.5, 12.750); } void mitkImageStatisticsCalculatorTestSuite::TestRecomputeOnModifiedMask() { MITK_INFO << std::endl << "TestRecomputeOnModifiedMask:-----------------------------------------------------------------------------------"; mitk::Image::Pointer mask_image = mitk::ImageGenerator::GenerateImageFromReference( m_TestImage, 0 ); mitk::ImageStatisticsCalculator::Pointer statisticsCalculator = mitk::ImageStatisticsCalculator::New(); statisticsCalculator->SetInputImage( m_TestImage ); mitk::ImageMaskGenerator::Pointer imgMaskGen = mitk::ImageMaskGenerator::New(); imgMaskGen->SetImageMask(mask_image); statisticsCalculator->SetMask(imgMaskGen.GetPointer()); this->VerifyStatistics( statisticsCalculator->GetStatistics(), -21474836.480, -21474836.480, -21474836.480); // activate voxel in the mask image if (mask_image->GetDimension() == 3) { itk::Index<3U> test_index = { { 11, 8, 0 } }; mitk::ImagePixelWriteAccessor< unsigned char, 3> writeAccess(mask_image); writeAccess.SetPixelByIndex(test_index, 1); } if (mask_image->GetDimension() == 4) { itk::Index<4U> test_index = { { 11, 8, 0, 0 } }; mitk::ImagePixelWriteAccessor< unsigned char, 4> writeAccess(mask_image); writeAccess.SetPixelByIndex(test_index, 1); } mask_image->Modified(); - mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stat = statisticsCalculator->GetStatistics(); + mitk::StatisticsContainer::Pointer stat = statisticsCalculator->GetStatistics(); this->VerifyStatistics( stat, 128.0, 0.0, 128.0); MITK_TEST_CONDITION( stat->GetN() == 1, "Calculated mask voxel count '" << stat->GetN() << "' is equal to the desired value '" << 1 << "'" ); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DStatistics() { MITK_INFO << std::endl << "Test plain Pic3D:-----------------------------------------------------------------------------------"; long expected_N = 3211264; double expected_mean = -365.80015345982144; double expected_MPP = 111.80226129535752; double expected_median = -105.16000366210938; double expected_skewness = -0.26976612134147004; double expected_kurtosis = 1.4655017209571437; double expected_uniformity = 0.06087994379480554; double expected_UPP = 0.011227934437026977; double expected_variance = 224036.80150510342; double expected_standarddev = 473.32525973700518; double expected_min = -1023; double expected_max = 1361; double expected_RMS = 598.20276978323352; double expected_entropy = 4.6727423654570357; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 0; expected_minIndex[1] = 0; expected_minIndex[2] = 0; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 139; expected_maxIndex[1] = 182; expected_maxIndex[2] = 43; - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DAxialPlanarFigureMaskStatistics() { MITK_INFO << std::endl << "Test Pic3D axial pf:-----------------------------------------------------------------------------------"; double expected_entropy = 5.6719817476387417; double expected_kurtosis = 5.8846935191205221; double expected_MPP = 230.43933685003768; double expected_max = 1206; double expected_mean = 182.30282131661443; double expected_median = 95.970001220703125; double expected_min = -156; long expected_N = 3190; double expected_RMS = 301.93844376702253; double expected_skewness = 1.6400489794326298; double expected_standarddev = 240.69172225993557; double expected_UPP = 0.024889790784288681; double expected_uniformity = 0.027579917650180332; double expected_variance = 57932.505164453964; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 156; expected_minIndex[1] = 133; expected_minIndex[2] = 24; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 125; expected_maxIndex[1] = 167; expected_maxIndex[2] = 24; mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_Pic3DImage); pfMaskGen->SetPlanarFigure(m_Pic3DPlanarFigureAxial); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, pfMaskGen.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, pfMaskGen.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DSagittalPlanarFigureMaskStatistics() { MITK_INFO << std::endl << "Test Pic3D sagittal pf:-----------------------------------------------------------------------------------"; double expected_entropy = 5.6051911962074286; double expected_kurtosis = 6.5814062739142338; double expected_MPP = 249.03202846975088; double expected_max = 1240; double expected_mean = 233.93602693602693; double expected_median = 174.9849853515625; double expected_min = -83; long expected_N = 1188; double expected_RMS = 332.03230188484594; double expected_skewness = 1.7489809015501814; double expected_standarddev = 235.62551813489128; double expected_UPP = 0.026837539253364174; double expected_uniformity = 0.027346982734188126; double expected_variance = 55519.384796335973; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 128; expected_minIndex[1] = 119; expected_minIndex[2] = 22; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 128; expected_maxIndex[1] = 167; expected_maxIndex[2] = 22; mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_Pic3DImage); pfMaskGen->SetPlanarFigure(m_Pic3DPlanarFigureSagittal); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, pfMaskGen.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, pfMaskGen.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DCoronalPlanarFigureMaskStatistics() { MITK_INFO << std::endl << "Test Pic3D coronal pf:-----------------------------------------------------------------------------------"; double expected_entropy = 6.0677398647867449; double expected_kurtosis = 1.6242929941303372; double expected_MPP = 76.649350649350652; double expected_max = 156; double expected_mean = -482.14807692307693; double expected_median = -660.07501220703125; double expected_min = -897; long expected_N = 520; double expected_RMS = 595.09446729069839; double expected_skewness = 0.51691492278851858; double expected_standarddev = 348.81321207686312; double expected_UPP = 0.0021560650887573964; double expected_uniformity = 0.020295857988165685; double expected_variance = 121670.6569193787; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 217; expected_minIndex[1] = 127; expected_minIndex[2] = 43; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 209; expected_maxIndex[1] = 127; expected_maxIndex[2] = 39; mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_Pic3DImage); pfMaskGen->SetPlanarFigure(m_Pic3DPlanarFigureCoronal); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, pfMaskGen.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, pfMaskGen.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DImageMaskStatistics_label1() { MITK_INFO << std::endl << "Test Pic3D image mask label 1 pf:-----------------------------------------------------------------------------------"; double expected_entropy = 5.695858251095868; double expected_kurtosis = 4.2728827997815717; double expected_MPP = 413.52408256880733; double expected_max = 1206; double expected_mean = 413.52408256880733; double expected_median = 324; double expected_min = 6; long expected_N = 872; double expected_RMS = 472.02024695145235; double expected_skewness = 1.3396074364415382; double expected_standarddev = 227.59821323493802; double expected_UPP = 0.029758648261930806; double expected_uniformity = 0.029758648261930806; double expected_variance = 51800.946667736309; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 135; expected_minIndex[1] = 158; expected_minIndex[2] = 24; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 125; expected_maxIndex[1] = 167; expected_maxIndex[2] = 24; mitk::ImageMaskGenerator::Pointer imgMaskGen = mitk::ImageMaskGenerator::New(); imgMaskGen->SetImageMask(m_Pic3DImageMask); imgMaskGen->SetInputImage(m_Pic3DImage); imgMaskGen->SetTimeStep(0); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, imgMaskGen.GetPointer(), nullptr, 1); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, imgMaskGen.GetPointer(), nullptr, 1); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DImageMaskStatistics_label2() { MITK_INFO << std::endl << "Test Pic3D image mask label 2 pf:-----------------------------------------------------------------------------------"; double expected_entropy = 4.3685781901212764; double expected_kurtosis = 9.7999112757587934; double expected_MPP = -nan(""); double expected_max = -145; double expected_mean = -897.92833876221493; double expected_median = -969.16499900817871; double expected_min = -1008; long expected_N = 307; double expected_RMS = 913.01496468179471; double expected_skewness = 2.6658524648889736; double expected_standarddev = 165.29072623903585; double expected_UPP = 0; double expected_uniformity = 0.087544695434434425; double expected_variance = 27321.024180627897; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 170; expected_minIndex[1] = 60; expected_minIndex[2] = 24; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 173; expected_maxIndex[1] = 57; expected_maxIndex[2] = 24; mitk::ImageMaskGenerator::Pointer imgMaskGen = mitk::ImageMaskGenerator::New(); imgMaskGen->SetImageMask(m_Pic3DImageMask); imgMaskGen->SetInputImage(m_Pic3DImage); imgMaskGen->SetTimeStep(0); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, imgMaskGen.GetPointer(), nullptr, 2); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, imgMaskGen.GetPointer(), nullptr, 2); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DIgnorePixelValueMaskStatistics() { MITK_INFO << std::endl << "Test Pic3D ignore zero pixels:-----------------------------------------------------------------------------------"; double expected_entropy = 4.671045011438645; double expected_kurtosis = 1.4638176488404484; double expected_MPP = 111.80226129535752; double expected_max = 1361; double expected_mean = -366.48547402877585; double expected_median = -105.16000366210938; double expected_min = -1023; long expected_N = 3205259; double expected_RMS = 598.76286909522139; double expected_skewness = -0.26648854845130782; double expected_standarddev = 473.50329537717545; double expected_UPP = 0.011270044547276429; double expected_uniformity = 0.061029773286547614; double expected_variance = 224205.37073304466; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 0; expected_minIndex[1] = 0; expected_minIndex[2] = 0; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 139; expected_maxIndex[1] = 182; expected_maxIndex[2] = 43; mitk::IgnorePixelMaskGenerator::Pointer ignPixelValMask = mitk::IgnorePixelMaskGenerator::New(); ignPixelValMask->SetInputImage(m_Pic3DImage); ignPixelValMask->SetIgnoredPixelValue(0); ignPixelValMask->SetTimeStep(0); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, ignPixelValMask.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, ignPixelValMask.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestPic3DSecondaryMaskStatistics() { MITK_INFO << std::endl << "Test Pic3D ignore zero pixels AND Image mask 2:-----------------------------------------------------------------------------------"; double expected_entropy = 5.9741637167320176; double expected_kurtosis = 3.490663358061596; double expected_MPP = 332.43534482758622; double expected_max = 1206; double expected_mean = 320.63333333333333; double expected_median = 265.06500244140625; double expected_min = -57; long expected_N = 720; double expected_RMS = 433.57749531594055; double expected_skewness = 1.1047775627624981; double expected_standarddev = 291.86248474238687; double expected_UPP = 0.020628858024691339; double expected_uniformity = 0.021377314814814797; double expected_variance = 85183.710000000006; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 116; expected_minIndex[1] = 170; expected_minIndex[2] = 24; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 125; expected_maxIndex[1] = 167; expected_maxIndex[2] = 24; mitk::IgnorePixelMaskGenerator::Pointer ignPixelValMask = mitk::IgnorePixelMaskGenerator::New(); ignPixelValMask->SetInputImage(m_Pic3DImage); ignPixelValMask->SetIgnoredPixelValue(0); ignPixelValMask->SetTimeStep(0); mitk::ImageMaskGenerator::Pointer imgMaskGen2 = mitk::ImageMaskGenerator::New(); imgMaskGen2->SetImageMask(m_Pic3DImageMask2); imgMaskGen2->SetInputImage(m_Pic3DImage); imgMaskGen2->SetTimeStep(0); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, imgMaskGen2.GetPointer(), ignPixelValMask.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_Pic3DImage, 0, imgMaskGen2.GetPointer(), ignPixelValMask.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylStatistics_time1() { MITK_INFO << std::endl << "Test plain US4D timeStep1:-----------------------------------------------------------------------------------"; double expected_entropy = 4.8272774900452502; double expected_kurtosis = 6.1336513352934432; double expected_MPP = 53.395358640738536; double expected_max = 199; double expected_mean = 35.771298153622375; double expected_median = 20.894999504089355; double expected_min = 0; long expected_N = 3409920; double expected_RMS = 59.244523377028408; double expected_skewness = 1.8734292240015058; double expected_standarddev = 47.226346233600559; double expected_UPP = 0.12098731125004937; double expected_uniformity = 0.12098731125004937; double expected_variance = 2230.3277785759178; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 0; expected_minIndex[1] = 0; expected_minIndex[2] = 0; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 268; expected_maxIndex[1] = 101; expected_maxIndex[2] = 0; - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylAxialPlanarFigureMaskStatistics_time1() { MITK_INFO << std::endl << "Test US4D axial pf timeStep1:-----------------------------------------------------------------------------------"; double expected_entropy = 6.218151288002292; double expected_kurtosis = 1.7322676370242023; double expected_MPP = 121.11663807890223; double expected_max = 199; double expected_mean = 121.11663807890223; double expected_median = 120.14999771118164; double expected_min = 9; long expected_N = 2332; double expected_RMS = 134.41895158590751; double expected_skewness = -0.1454808104597369; double expected_standarddev = 58.30278317472294; double expected_UPP = 0.021354765820606133; double expected_uniformity = 0.021354765820606133; double expected_variance = 3399.214525918756; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 129; expected_minIndex[1] = 131; expected_minIndex[2] = 19; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 126; expected_maxIndex[1] = 137; expected_maxIndex[2] = 19; mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_US4DImage); pfMaskGen->SetPlanarFigure(m_US4DPlanarFigureAxial); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, pfMaskGen.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, pfMaskGen.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylSagittalPlanarFigureMaskStatistics_time1() { MITK_INFO << std::endl << "Test US4D sagittal pf timeStep1:-----------------------------------------------------------------------------------"; double expected_entropy = 5.2003987046387508; double expected_kurtosis = 2.7574491062430142; double expected_MPP = 26.212534059945504; double expected_max = 59; double expected_mean = 26.176870748299319; double expected_median = 26.254999160766602; double expected_min = 0; long expected_N = 735; double expected_RMS = 28.084905283121476; double expected_skewness = 0.18245181360752327; double expected_standarddev = 10.175133541567705; double expected_UPP = 0.032921467906890628; double expected_uniformity = 0.032921467906890628; double expected_variance = 103.53334258873615; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 147; expected_minIndex[1] = 94; expected_minIndex[2] = 21; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 147; expected_maxIndex[1] = 77; expected_maxIndex[2] = 24; mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_US4DImage); pfMaskGen->SetPlanarFigure(m_US4DPlanarFigureSagittal); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, pfMaskGen.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, pfMaskGen.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylCoronalPlanarFigureMaskStatistics_time1() { MITK_INFO << std::endl << "Test US4D coronal pf timeStep1:-----------------------------------------------------------------------------------"; double expected_entropy = 5.8892941136639161; double expected_kurtosis = 4.6434920707409564; double expected_MPP = 55.486426346239433; double expected_max = 199; double expected_mean = 55.118479221927501; double expected_median = 36.815000534057617; double expected_min = 0; long expected_N = 2262; double expected_RMS = 71.98149752438627; double expected_skewness = 1.4988288344523237; double expected_standarddev = 46.29567187238105; double expected_UPP = 0.023286748110675673; double expected_uniformity = 0.023286748110675673; double expected_variance = 2143.2892341151742; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 214; expected_minIndex[1] = 169; expected_minIndex[2] = 10; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 99; expected_maxIndex[1] = 169; expected_maxIndex[2] = 17; mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_US4DImage); pfMaskGen->SetPlanarFigure(m_US4DPlanarFigureCoronal); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, pfMaskGen.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, pfMaskGen.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylImageMaskStatistics_time1_label_1() { MITK_INFO << std::endl << "Test US4D image mask time 1 label 1:-----------------------------------------------------------------------------------"; double expected_entropy = 5.0082903903398677; double expected_kurtosis = 3.6266994778237809; double expected_MPP = 169.58938547486034; double expected_max = 199; double expected_mean = 169.58938547486034; double expected_median = 187.44000244140625; double expected_min = 63; long expected_N = 716; double expected_RMS = 173.09843164831432; double expected_skewness = -1.2248969838579555; double expected_standarddev = 34.677188083311712; double expected_UPP = 0.076601073624418703; double expected_uniformity = 0.076601073624418703; double expected_variance = 1202.5073733653758; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 82; expected_minIndex[1] = 158; expected_minIndex[2] = 19; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 126; expected_maxIndex[1] = 140; expected_maxIndex[2] = 19; mitk::ImageMaskGenerator::Pointer imgMask1 = mitk::ImageMaskGenerator::New(); imgMask1->SetInputImage(m_US4DImage); imgMask1->SetImageMask(m_US4DImageMask); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, imgMask1.GetPointer(), nullptr, 1); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, imgMask1.GetPointer(), nullptr, 1); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylImageMaskStatistics_time2_label_1() { MITK_INFO << std::endl << "Test US4D image mask time 2 label 1:-----------------------------------------------------------------------------------"; double expected_entropy = 5.1857604214916506; double expected_kurtosis = 3.0692303858330683; double expected_MPP = 167.97194163860831; double expected_max = 199; double expected_mean = 167.97194163860831; double expected_median = 184.39499664306641; double expected_min = 72; long expected_N = 891; double expected_RMS = 171.67986611998634; double expected_skewness = -1.1221651136259736; double expected_standarddev = 35.488071983870803; double expected_UPP = 0.063124070232188439; double expected_uniformity = 0.063124070232188439; double expected_variance = 1259.4032531323958; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 103; expected_minIndex[1] = 212; expected_minIndex[2] = 19; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 102; expected_maxIndex[1] = 168; expected_maxIndex[2] = 19; mitk::ImageMaskGenerator::Pointer imgMask1 = mitk::ImageMaskGenerator::New(); imgMask1->SetInputImage(m_US4DImage); imgMask1->SetImageMask(m_US4DImageMask); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 2, imgMask1.GetPointer(), nullptr, 1); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 2, imgMask1.GetPointer(), nullptr, 1); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylImageMaskStatistics_time1_label_2() { MITK_INFO << std::endl << "Test US4D image mask time 1 label 2:-----------------------------------------------------------------------------------"; double expected_entropy = 5.0822234230119001; double expected_kurtosis = 2.4346603343623747; double expected_MPP = 20.733626373626375; double expected_max = 46; double expected_mean = 20.624836029733274; double expected_median = 20.010000228881836; double expected_min = 0; long expected_N = 2287; double expected_RMS = 22.508347574573804; double expected_skewness = 0.13837218490626488; double expected_standarddev = 9.0134260569684965; double expected_UPP = 0.034783970308787; double expected_uniformity = 0.034783970308787; double expected_variance = 81.241849284438644; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 178; expected_minIndex[1] = 76; expected_minIndex[2] = 19; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 198; expected_maxIndex[1] = 90; expected_maxIndex[2] = 19; mitk::ImageMaskGenerator::Pointer imgMask1 = mitk::ImageMaskGenerator::New(); imgMask1->SetInputImage(m_US4DImage); imgMask1->SetImageMask(m_US4DImageMask); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, imgMask1.GetPointer(), nullptr, 2); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, imgMask1.GetPointer(), nullptr, 2); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylIgnorePixelValueMaskStatistics_time1() { MITK_INFO << std::endl << "Test US4D ignore zero pixels:-----------------------------------------------------------------------------------"; double expected_entropy = 5.8609813848087962; double expected_kurtosis = 4.7556214582883651; double expected_MPP = 53.395358640738536; double expected_max = 199; double expected_mean = 53.395358640738536; double expected_median = 35.649999618530273; double expected_min = 1; long expected_N = 2284417; double expected_RMS = 72.382339046507084; double expected_skewness = 1.588289859859108; double expected_standarddev = 48.868585834566694; double expected_UPP = 0.023927063695115193; double expected_uniformity = 0.023927063695115193; double expected_variance = 2388.1386814704128; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 187; expected_minIndex[1] = 19; expected_minIndex[2] = 0; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 268; expected_maxIndex[1] = 101; expected_maxIndex[2] = 0; mitk::IgnorePixelMaskGenerator::Pointer ignPixelValMask = mitk::IgnorePixelMaskGenerator::New(); ignPixelValMask->SetInputImage(m_US4DImage); ignPixelValMask->SetIgnoredPixelValue(0); ignPixelValMask->SetTimeStep(1); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, ignPixelValMask.GetPointer()); - //std::cout << result->GetAsString(); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, ignPixelValMask.GetPointer()); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } void mitkImageStatisticsCalculatorTestSuite::TestUS4DCylSecondaryMaskStatistics_time1() { MITK_INFO << std::endl << "Test US4d ignore zero pixels AND Image mask 2:-----------------------------------------------------------------------------------"; double expected_entropy = 4.9955858614274558; double expected_kurtosis = 17.471042803365179; double expected_MPP = 32.791403286978507; double expected_max = 199; double expected_mean = 32.791403286978507; double expected_median = 25.75; double expected_min = 1; long expected_N = 17402; double expected_RMS = 42.776697859745241; double expected_skewness = 3.3991813038552596; double expected_standarddev = 27.469433016621732; double expected_UPP = 0.043040554251756687; double expected_uniformity = 0.043040554251756687; double expected_variance = 754.56975025466807; vnl_vector expected_minIndex; expected_minIndex.set_size(3); expected_minIndex[0] = 177; expected_minIndex[1] = 27; expected_minIndex[2] = 36; vnl_vector expected_maxIndex; expected_maxIndex.set_size(3); expected_maxIndex[0] = 109; expected_maxIndex[1] = 116; expected_maxIndex[2] = 36; mitk::IgnorePixelMaskGenerator::Pointer ignPixelValMask = mitk::IgnorePixelMaskGenerator::New(); ignPixelValMask->SetInputImage(m_US4DImage); ignPixelValMask->SetIgnoredPixelValue(0); mitk::ImageMaskGenerator::Pointer imgMaskGen2 = mitk::ImageMaskGenerator::New(); imgMaskGen2->SetImageMask(m_US4DImageMask2); imgMaskGen2->SetInputImage(m_US4DImage); - const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, imgMaskGen2.GetPointer(), ignPixelValMask.GetPointer()); + const mitk::StatisticsContainer::Pointer result = ComputeStatisticsNew(m_US4DImage, 1, imgMaskGen2.GetPointer(), ignPixelValMask.GetPointer()); //std::cout << result->GetAsString(); VerifyStatistics(result, expected_N, expected_mean, expected_MPP, expected_median, expected_skewness, expected_kurtosis, expected_uniformity, expected_UPP, expected_variance, expected_standarddev, expected_min, expected_max, expected_RMS, expected_entropy, expected_minIndex, expected_maxIndex); } -const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer +const mitk::StatisticsContainer::Pointer mitkImageStatisticsCalculatorTestSuite::ComputeStatistics( mitk::Image::Pointer image, mitk::PlanarFigure::Pointer polygon ) { mitk::ImageStatisticsCalculator::Pointer statisticsCalculator = mitk::ImageStatisticsCalculator::New(); statisticsCalculator->SetInputImage( image ); statisticsCalculator->SetNBinsForHistogramStatistics(10); mitk::PlanarFigureMaskGenerator::Pointer planFigMaskGen = mitk::PlanarFigureMaskGenerator::New(); planFigMaskGen->SetInputImage(image); planFigMaskGen->SetPlanarFigure(polygon); statisticsCalculator->SetMask(planFigMaskGen.GetPointer()); try { return statisticsCalculator->GetStatistics(); } catch( ... ) { } - return mitk::ImageStatisticsCalculator::StatisticsContainer::New(); + return mitk::StatisticsContainer::New(); } -const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer +const mitk::StatisticsContainer::Pointer mitkImageStatisticsCalculatorTestSuite::ComputeStatistics(mitk::Image::Pointer image, mitk::Image::Pointer image_mask ) { mitk::ImageStatisticsCalculator::Pointer statisticsCalculator = mitk::ImageStatisticsCalculator::New(); statisticsCalculator->SetInputImage(image); statisticsCalculator->SetNBinsForHistogramStatistics(10); mitk::ImageMaskGenerator::Pointer imgMaskGen = mitk::ImageMaskGenerator::New(); imgMaskGen->SetImageMask(image_mask); statisticsCalculator->SetMask(imgMaskGen.GetPointer()); return statisticsCalculator->GetStatistics(); } -const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer +const mitk::StatisticsContainer::Pointer mitkImageStatisticsCalculatorTestSuite::ComputeStatisticsNew(mitk::Image::Pointer image, int timeStep, mitk::MaskGenerator::Pointer maskGen, mitk::MaskGenerator::Pointer secondardMaskGen, unsigned short label) { mitk::ImageStatisticsCalculator::Pointer imgStatCalc = mitk::ImageStatisticsCalculator::New(); imgStatCalc->SetInputImage(image); if (maskGen.IsNotNull()) { imgStatCalc->SetMask(maskGen.GetPointer()); if (secondardMaskGen.IsNotNull()) { imgStatCalc->SetSecondaryMask(secondardMaskGen.GetPointer()); } } return imgStatCalc->GetStatistics(timeStep, label); } -void mitkImageStatisticsCalculatorTestSuite::VerifyStatistics(mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats, +void mitkImageStatisticsCalculatorTestSuite::VerifyStatistics(mitk::StatisticsContainer::Pointer stats, double testMean, double testSD, double testMedian) { int tmpMean = stats->GetMean() * 100; double calculatedMean = tmpMean / 100.0; MITK_TEST_CONDITION( calculatedMean == testMean, "Calculated mean grayvalue '" << calculatedMean << "' is equal to the desired value '" << testMean << "'" ); int tmpSD = stats->GetStd() * 100; double calculatedSD = tmpSD / 100.0; MITK_TEST_CONDITION( calculatedSD == testSD, "Calculated grayvalue sd '" << calculatedSD << "' is equal to the desired value '" << testSD <<"'" ); int tmpMedian = stats->GetMedian() * 100; double calculatedMedian = tmpMedian / 100.0; MITK_TEST_CONDITION( testMedian == calculatedMedian, "Calculated median grayvalue '" << calculatedMedian << "' is equal to the desired value '" << testMedian << "'"); } -void mitkImageStatisticsCalculatorTestSuite::VerifyStatistics(mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats, +void mitkImageStatisticsCalculatorTestSuite::VerifyStatistics(mitk::StatisticsContainer::Pointer stats, long N, double mean, double MPP, double median, double skewness, double kurtosis, double uniformity, double UPP, double variance, double stdev, double min, double max, double RMS, double entropy, vnl_vector minIndex, vnl_vector maxIndex) { MITK_TEST_CONDITION(std::abs(stats->GetN() - N) < mitk::eps, "calculated N: " << stats->GetN() << " expected N: " << N); MITK_TEST_CONDITION(std::abs(stats->GetMean() - mean) < mitk::eps, "calculated mean: " << stats->GetMean() << " expected mean: " << mean); // in one test case MPP is None because the roi has no positive pixels if (!std::isnan(stats->GetMPP())) { MITK_TEST_CONDITION(std::abs(stats->GetMPP() - MPP) < mitk::eps, "calculated MPP: " << stats->GetMPP() << " expected MPP: " << MPP); } MITK_TEST_CONDITION(std::abs(stats->GetMedian() - median) < mitk::eps, "calculated median: " << stats->GetMedian() << " expected median: " << median); MITK_TEST_CONDITION(std::abs(stats->GetSkewness() - skewness) < mitk::eps, "calculated skewness: " << stats->GetSkewness() << " expected skewness: " << skewness); MITK_TEST_CONDITION(std::abs(stats->GetKurtosis() - kurtosis) < mitk::eps, "calculated kurtosis: " << stats->GetKurtosis() << " expected kurtosis: " << kurtosis); MITK_TEST_CONDITION(std::abs(stats->GetUniformity() - uniformity) < mitk::eps, "calculated uniformity: " << stats->GetUniformity() << " expected uniformity: " << uniformity); MITK_TEST_CONDITION(std::abs(stats->GetUPP() - UPP) < mitk::eps, "calculated UPP: " << stats->GetUPP() << " expected UPP: " << UPP); MITK_TEST_CONDITION(std::abs(stats->GetVariance() - variance) < mitk::eps, "calculated variance: " << stats->GetVariance() << " expected variance: " << variance); MITK_TEST_CONDITION(std::abs(stats->GetStd() - stdev) < mitk::eps, "calculated stdev: " << stats->GetStd() << " expected stdev: " << stdev); MITK_TEST_CONDITION(std::abs(stats->GetMin() - min) < mitk::eps, "calculated min: " << stats->GetMin() << " expected min: " << min); MITK_TEST_CONDITION(std::abs(stats->GetMax() - max) < mitk::eps, "calculated max: " << stats->GetMax() << " expected max: " << max); MITK_TEST_CONDITION(std::abs(stats->GetRMS() - RMS) < mitk::eps, "calculated RMS: " << stats->GetRMS() << " expected RMS: " << RMS); MITK_TEST_CONDITION(std::abs(stats->GetEntropy() - entropy) < mitk::eps, "calculated entropy: " << stats->GetEntropy() << " expected entropy: " << entropy); for (unsigned int i = 0; i < minIndex.size(); ++i) { MITK_TEST_CONDITION(std::abs(stats->GetMinIndex()[i] - minIndex[i]) < mitk::eps, "minIndex [" << i << "] = " << stats->GetMinIndex()[i] << " expected: " << minIndex[i]); } for (unsigned int i = 0; i < maxIndex.size(); ++i) { MITK_TEST_CONDITION(std::abs(stats->GetMaxIndex()[i] - maxIndex[i]) < mitk::eps, "maxIndex [" << i << "] = " << stats->GetMaxIndex()[i] << " expected: " << maxIndex[i]); } } void mitkImageStatisticsCalculatorTestSuite::TestUninitializedImage() { /***************************** * loading uninitialized image to datastorage ******************************/ MITK_INFO << std::endl << "Test uninitialized image: -----------------------------------------------------------------------------------"; MITK_TEST_FOR_EXCEPTION_BEGIN(mitk::Exception) mitk::Image::Pointer image = mitk::Image::New(); mitk::DataNode::Pointer node = mitk::DataNode::New(); node->SetData(image); mitk::ImageStatisticsCalculator::Pointer is = mitk::ImageStatisticsCalculator::New(); is->GetStatistics(); MITK_TEST_FOR_EXCEPTION_END(mitk::Exception) } MITK_TEST_SUITE_REGISTRATION(mitkImageStatisticsCalculator) diff --git a/Modules/ImageStatistics/Testing/mitkImageStatisticsHotspotTest.cpp b/Modules/ImageStatistics/Testing/mitkImageStatisticsHotspotTest.cpp index c417798f23..9f2561e0ff 100644 --- a/Modules/ImageStatistics/Testing/mitkImageStatisticsHotspotTest.cpp +++ b/Modules/ImageStatistics/Testing/mitkImageStatisticsHotspotTest.cpp @@ -1,651 +1,651 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkImageStatisticsCalculator.h" #include "itkMultiGaussianImageSource.h" #include "mitkTestingMacros.h" #include "mitkImageCast.h" #include #include #include #include #include #include #include /** \section hotspotCalculationTestCases Testcases To see the different Hotspot-Testcases have a look at the \ref hotspottestdoc. Note from an intensive session of checking the test results: - itk::MultiGaussianImageSource needs a review - the test idea is ok, but the combination of XML files for parameters and MultiGaussianImageSource has serious flaws - the XML file should contain exactly the parameters that MultiGaussianImageSource requires - in contrast, now the XML file mentions index coordinates for gaussian centers while the MultiGaussianImageSource expects world coordinates - this requires a transformation (index * spacing assuming no rotation) that was actually broken until recently */ struct mitkImageStatisticsHotspotTestClass { /** \brief Test parameters for one test case. Describes all aspects of a single test case: - parameters to generate a test image - parameters of a ROI that describes where to calculate statistics - expected statistics results */ struct Parameters { public: // XML-Tag /** \brief XML-Tag "image-rows": size of x-dimension */ int m_ImageRows; /** \brief XML-Tag "image-columns": size of y-dimension */ int m_ImageColumns; /** \brief XML-Tag "image-slices": size of z-dimension */ int m_ImageSlices; /** \brief XML-Tag "numberOfGaussians": number of used gauss-functions */ int m_NumberOfGaussian; /** \brief XML-Tags "spacingX", "spacingY", "spacingZ": spacing of image in every direction */ double m_Spacing[3]; /** \brief XML-Tag "entireHotSpotInImage" */ unsigned int m_EntireHotspotInImage; // XML-Tag /** \brief XML-Tag "centerIndexX: gaussian parameter \warning This parameter READS the centerIndexX parameter from file and is THEN MISUSED to calculate some position in world coordinates, so we require double. */ std::vector m_CenterX; /** \brief XML-Tag "centerIndexY: gaussian parameter \warning This parameter READS the centerIndexX parameter from file and is THEN MISUSED to calculate some position in world coordinates, so we require double. */ std::vector m_CenterY; /** \brief XML-Tag "centerIndexZ: gaussian parameter \warning This parameter READS the centerIndexX parameter from file and is THEN MISUSED to calculate some position in world coordinates, so we require double. */ std::vector m_CenterZ; /** \brief XML-Tag "deviationX: gaussian parameter */ std::vector m_SigmaX; /** \brief XML-Tag "deviationY: gaussian parameter */ std::vector m_SigmaY; /** \brief XML-Tag "deviationZ: gaussian parameter */ std::vector m_SigmaZ; /** \brief XML-Tag "altitude: gaussian parameter */ std::vector m_Altitude; // XML-Tag /** \brief XML-Tag "numberOfLabels": number of different labels which appear in the mask */ unsigned int m_NumberOfLabels; /** \brief XML-Tag "hotspotRadiusInMM": radius of hotspot */ double m_HotspotRadiusInMM; // XML-Tag /** \brief XML-Tag "maximumSizeX": maximum position of ROI in x-dimension */ vnl_vector m_MaxIndexX; /** \brief XML-Tag "minimumSizeX": minimum position of ROI in x-dimension */ vnl_vector m_MinIndexX; /** \brief XML-Tag "maximumSizeX": maximum position of ROI in y-dimension */ vnl_vector m_MaxIndexY; /** \brief XML-Tag "minimumSizeX": minimum position of ROI in y-dimension */ vnl_vector m_MinIndexY; /** \brief XML-Tag "maximumSizeX": maximum position of ROI in z-dimension */ vnl_vector m_MaxIndexZ; /** \brief XML-Tag "minimumSizeX": minimum position of ROI in z-dimension */ vnl_vector m_MinIndexZ; /** \brief XML-Tag "label": value of label */ vnl_vector m_Label; //XML-Tag /** \brief XML-Tag "minimum": minimum inside hotspot */ vnl_vector m_HotspotMin; /** \brief XML-Tag "maximum": maximum inside hotspot */ vnl_vector m_HotspotMax; /** \brief XML-Tag "mean": mean value of hotspot */ vnl_vector m_HotspotMean; /** \brief XML-Tag "maximumIndexX": x-coordinate of maximum-location inside hotspot */ vnl_vector m_HotspotMaxIndexX; /** \brief XML-Tag "maximumIndexX": y-coordinate of maximum-location inside hotspot */ vnl_vector m_HotspotMaxIndexY; /** \brief XML-Tag "maximumIndexX": z-coordinate of maximum-location inside hotspot */ vnl_vector m_HotspotMaxIndexZ; /** \brief XML-Tag "maximumIndexX": x-coordinate of maximum-location inside hotspot */ vnl_vector m_HotspotMinIndexX; /** \brief XML-Tag "maximumIndexX": y-coordinate of maximum-location inside hotspot */ vnl_vector m_HotspotMinIndexY; /** \brief XML-Tag "maximumIndexX": z-coordinate of maximum-location inside hotspot */ vnl_vector m_HotspotMinIndexZ; /** \brief XML-Tag "maximumIndexX": x-coordinate of hotspot-location */ vnl_vector m_HotspotIndexX; /** \brief XML-Tag "maximumIndexX": y-coordinate of hotspot-location */ vnl_vector m_HotspotIndexY; /** \brief XML-Tag "maximumIndexX": z-coordinate of hotspot-location */ vnl_vector m_HotspotIndexZ; }; /** \brief Find/Convert integer attribute in itk::DOMNode. */ static int GetIntegerAttribute(itk::DOMNode* domNode, const std::string& tag) { assert(domNode); MITK_TEST_CONDITION_REQUIRED( domNode->HasAttribute(tag), "Tag '" << tag << "' is defined in test parameters" ); std::string attributeValue = domNode->GetAttribute(tag); int resultValue; try { //MITK_TEST_OUTPUT( << "Converting tag value '" << attributeValue << "' for tag '" << tag << "' to integer"); std::stringstream(attributeValue) >> resultValue; return resultValue; } catch(std::exception& /*e*/) { MITK_TEST_CONDITION_REQUIRED(false, "Convert tag value '" << attributeValue << "' for tag '" << tag << "' to integer"); return 0; // just to satisfy compiler } } /** \brief Find/Convert double attribute in itk::DOMNode. */ static double GetDoubleAttribute(itk::DOMNode* domNode, const std::string& tag) { assert(domNode); MITK_TEST_CONDITION_REQUIRED( domNode->HasAttribute(tag), "Tag '" << tag << "' is defined in test parameters" ); std::string attributeValue = domNode->GetAttribute(tag); double resultValue; try { //MITK_TEST_OUTPUT( << "Converting tag value '" << attributeValue << "' for tag '" << tag << "' to double"); std::stringstream(attributeValue) >> resultValue; return resultValue; } catch(std::exception& /*e*/) { MITK_TEST_CONDITION_REQUIRED(false, "Convert tag value '" << attributeValue << "' for tag '" << tag << "' to double"); return 0.0; // just to satisfy compiler } } /** \brief Read XML file describing the test parameters. Reads XML file given in first commandline parameter in order to construct a Parameters structure. The XML file should be structurs as the following example, i.e. we describe the three test aspects of Parameters in four different tags, with all the details described as tag attributes. */ /** \verbatim \endverbatim */ static Parameters ParseParameters(int argc, char* argv[]) { MITK_TEST_CONDITION_REQUIRED(argc == 2, "Test is invoked with exactly 1 parameter (XML parameters file)"); MITK_INFO << "Reading parameters from file '" << argv[1] << "'"; std::string filename = argv[1]; Parameters result; itk::DOMNodeXMLReader::Pointer xmlReader = itk::DOMNodeXMLReader::New(); xmlReader->SetFileName( filename ); try { xmlReader->Update(); itk::DOMNode::Pointer domRoot = xmlReader->GetOutput(); typedef std::vector NodeList; NodeList testimages; domRoot->GetChildren("testimage", testimages); MITK_TEST_CONDITION_REQUIRED( testimages.size() == 1, "One test image defined" ) itk::DOMNode* testimage = testimages[0]; result.m_ImageRows = GetIntegerAttribute( testimage, "image-rows" ); result.m_ImageColumns = GetIntegerAttribute( testimage, "image-columns" ); result.m_ImageSlices = GetIntegerAttribute( testimage, "image-slices" ); result.m_NumberOfGaussian = GetIntegerAttribute( testimage, "numberOfGaussians" ); result.m_Spacing[0] = GetDoubleAttribute(testimage, "spacingX"); result.m_Spacing[1] = GetDoubleAttribute(testimage, "spacingY"); result.m_Spacing[2] = GetDoubleAttribute(testimage, "spacingZ"); result.m_EntireHotspotInImage = GetIntegerAttribute( testimage, "entireHotSpotInImage" ); MITK_TEST_OUTPUT( << "Read size parameters (x,y,z): " << result.m_ImageRows << "," << result.m_ImageColumns << "," << result.m_ImageSlices); MITK_TEST_OUTPUT( << "Read spacing parameters (x,y,z): " << result.m_Spacing[0] << "," << result.m_Spacing[1] << "," << result.m_Spacing[2]); NodeList gaussians; testimage->GetChildren("gaussian", gaussians); MITK_TEST_CONDITION_REQUIRED( gaussians.size() >= 1, "At least one gaussian is defined" ) result.m_CenterX.resize(result.m_NumberOfGaussian); result.m_CenterY.resize(result.m_NumberOfGaussian); result.m_CenterZ.resize(result.m_NumberOfGaussian); result.m_SigmaX.resize(result.m_NumberOfGaussian); result.m_SigmaY.resize(result.m_NumberOfGaussian); result.m_SigmaZ.resize(result.m_NumberOfGaussian); result.m_Altitude.resize(result.m_NumberOfGaussian); for(int i = 0; i < result.m_NumberOfGaussian ; ++i) { itk::DOMNode* gaussian = gaussians[i]; result.m_CenterX[i] = GetIntegerAttribute(gaussian, "centerIndexX"); result.m_CenterY[i] = GetIntegerAttribute(gaussian, "centerIndexY"); result.m_CenterZ[i] = GetIntegerAttribute(gaussian, "centerIndexZ"); result.m_SigmaX[i] = GetDoubleAttribute(gaussian, "deviationX"); result.m_SigmaY[i] = GetDoubleAttribute(gaussian, "deviationY"); result.m_SigmaZ[i] = GetDoubleAttribute(gaussian, "deviationZ"); result.m_Altitude[i] = GetDoubleAttribute(gaussian, "altitude"); result.m_CenterX[i] = result.m_CenterX[i] * result.m_Spacing[0]; result.m_CenterY[i] = result.m_CenterY[i] * result.m_Spacing[1]; result.m_CenterZ[i] = result.m_CenterZ[i] * result.m_Spacing[2]; result.m_SigmaX[i] = result.m_SigmaX[i] * result.m_Spacing[0]; result.m_SigmaY[i] = result.m_SigmaY[i] * result.m_Spacing[1]; result.m_SigmaZ[i] = result.m_SigmaZ[i] * result.m_Spacing[2]; } NodeList segmentations; domRoot->GetChildren("segmentation", segmentations); MITK_TEST_CONDITION_REQUIRED( segmentations.size() == 1, "One segmentation defined"); itk::DOMNode* segmentation = segmentations[0]; result.m_NumberOfLabels = GetIntegerAttribute(segmentation, "numberOfLabels"); result.m_HotspotRadiusInMM = GetDoubleAttribute(segmentation, "hotspotRadiusInMM"); // read ROI parameters, fill result structure NodeList rois; segmentation->GetChildren("roi", rois); MITK_TEST_CONDITION_REQUIRED( rois.size() >= 1, "At least one ROI defined" ) result.m_MaxIndexX.set_size(result.m_NumberOfLabels); result.m_MinIndexX.set_size(result.m_NumberOfLabels); result.m_MaxIndexY.set_size(result.m_NumberOfLabels); result.m_MinIndexY.set_size(result.m_NumberOfLabels); result.m_MaxIndexZ.set_size(result.m_NumberOfLabels); result.m_MinIndexZ.set_size(result.m_NumberOfLabels); result.m_Label.set_size(result.m_NumberOfLabels); for(unsigned int i = 0; i < rois.size(); ++i) { result.m_MaxIndexX[i] = GetIntegerAttribute(rois[i], "maximumIndexX"); result.m_MinIndexX[i] = GetIntegerAttribute(rois[i], "minimumIndexX"); result.m_MaxIndexY[i] = GetIntegerAttribute(rois[i], "maximumIndexY"); result.m_MinIndexY[i] = GetIntegerAttribute(rois[i], "minimumIndexY"); result.m_MaxIndexZ[i] = GetIntegerAttribute(rois[i], "maximumIndexZ"); result.m_MinIndexZ[i] = GetIntegerAttribute(rois[i], "minimumIndexZ"); result.m_Label[i] = GetIntegerAttribute(rois[i], "label"); } // read statistic parameters, fill result structure NodeList statistics; domRoot->GetChildren("statistic", statistics); MITK_TEST_CONDITION_REQUIRED( statistics.size() >= 1 , "At least one statistic defined" ) MITK_TEST_CONDITION_REQUIRED( statistics.size() == rois.size(), "Same number of rois and corresponding statistics defined"); result.m_HotspotMin.set_size(statistics.size()); result.m_HotspotMax.set_size(statistics.size()); result.m_HotspotMean.set_size(statistics.size()); result.m_HotspotMinIndexX.set_size(statistics.size()); result.m_HotspotMinIndexY.set_size(statistics.size()); result.m_HotspotMinIndexZ.set_size(statistics.size()); result.m_HotspotMaxIndexX.set_size(statistics.size()); result.m_HotspotMaxIndexY.set_size(statistics.size()); result.m_HotspotMaxIndexZ.set_size(statistics.size()); result.m_HotspotIndexX.set_size(statistics.size()); result.m_HotspotIndexY.set_size(statistics.size()); result.m_HotspotIndexZ.set_size(statistics.size()); for(unsigned int i = 0; i < statistics.size(); ++i) { result.m_HotspotMin[i] = GetDoubleAttribute(statistics[i], "minimum"); result.m_HotspotMax[i] = GetDoubleAttribute(statistics[i], "maximum"); result.m_HotspotMean[i] = GetDoubleAttribute(statistics[i], "mean"); result.m_HotspotMinIndexX[i] = GetIntegerAttribute(statistics[i], "minimumIndexX"); result.m_HotspotMinIndexY[i] = GetIntegerAttribute(statistics[i], "minimumIndexY"); result.m_HotspotMinIndexZ[i] = GetIntegerAttribute(statistics[i], "minimumIndexZ"); result.m_HotspotMaxIndexX[i] = GetIntegerAttribute(statistics[i], "maximumIndexX"); result.m_HotspotMaxIndexY[i] = GetIntegerAttribute(statistics[i], "maximumIndexY"); result.m_HotspotMaxIndexZ[i] = GetIntegerAttribute(statistics[i], "maximumIndexZ"); result.m_HotspotIndexX[i] = GetIntegerAttribute(statistics[i], "hotspotIndexX"); result.m_HotspotIndexY[i] = GetIntegerAttribute(statistics[i], "hotspotIndexY"); result.m_HotspotIndexZ[i] = GetIntegerAttribute(statistics[i], "hotspotIndexZ"); } } catch (std::exception& e) { MITK_TEST_CONDITION_REQUIRED(false, "Reading test parameters from XML file. Error message: " << e.what()); } return result; } /** \brief Generate an image that contains a couple of 3D gaussian distributions. Uses the given parameters to produce a test image using class MultiGaussianImageSource. */ static mitk::Image::Pointer BuildTestImage(const Parameters& testParameters) { mitk::Image::Pointer result; typedef double PixelType; const int Dimension = 3; typedef itk::Image ImageType; ImageType::Pointer image = ImageType::New(); typedef itk::MultiGaussianImageSource< ImageType > MultiGaussianImageSource; MultiGaussianImageSource::Pointer gaussianGenerator = MultiGaussianImageSource::New(); ImageType::SizeValueType size[3]; size[0] = testParameters.m_ImageColumns; size[1] = testParameters.m_ImageRows; size[2] = testParameters.m_ImageSlices; itk::MultiGaussianImageSource::VectorType centerXVec, centerYVec, centerZVec, sigmaXVec, sigmaYVec, sigmaZVec, altitudeVec; for(int i = 0; i < testParameters.m_NumberOfGaussian; ++i) { centerXVec.push_back(testParameters.m_CenterX[i]); centerYVec.push_back(testParameters.m_CenterY[i]); centerZVec.push_back(testParameters.m_CenterZ[i]); sigmaXVec.push_back(testParameters.m_SigmaX[i]); sigmaYVec.push_back(testParameters.m_SigmaY[i]); sigmaZVec.push_back(testParameters.m_SigmaZ[i]); altitudeVec.push_back(testParameters.m_Altitude[i]); } ImageType::SpacingType spacing; for( int i = 0; i < Dimension; ++i ) spacing[i] = testParameters.m_Spacing[i]; gaussianGenerator->SetSize( size ); gaussianGenerator->SetSpacing( spacing ); gaussianGenerator->SetRadius(testParameters.m_HotspotRadiusInMM); gaussianGenerator->SetNumberOfGausssians(testParameters.m_NumberOfGaussian); gaussianGenerator->AddGaussian(centerXVec, centerYVec, centerZVec, sigmaXVec, sigmaYVec, sigmaZVec, altitudeVec); gaussianGenerator->Update(); image = gaussianGenerator->GetOutput(); mitk::CastToMitkImage(image, result); return result; } /** \brief Calculates hotspot statistics for given test image and ROI parameters. Uses ImageStatisticsCalculator to find a hotspot in a defined ROI within the given image. */ - static mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer CalculateStatistics(mitk::Image* image, const Parameters& testParameters, unsigned int label) + static mitk::StatisticsContainer::Pointer CalculateStatistics(mitk::Image* image, const Parameters& testParameters, unsigned int label) { - mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer result; + mitk::StatisticsContainer::Pointer result; const unsigned int Dimension = 3; typedef itk::Image MaskImageType; MaskImageType::Pointer mask = MaskImageType::New(); MaskImageType::SizeType size; MaskImageType::SpacingType spacing; MaskImageType::IndexType start; mitk::ImageStatisticsCalculator::Pointer statisticsCalculator = mitk::ImageStatisticsCalculator::New(); statisticsCalculator->SetInputImage(image); mitk::Image::Pointer mitkMaskImage; if((testParameters.m_MaxIndexX[label] > testParameters.m_MinIndexX[label] && testParameters.m_MinIndexX[label] >= 0) && (testParameters.m_MaxIndexY[label] > testParameters.m_MinIndexY[label] && testParameters.m_MinIndexY[label] >= 0) && (testParameters.m_MaxIndexZ[label] > testParameters.m_MinIndexZ[label] && testParameters.m_MinIndexZ[label] >= 0)) { for(unsigned int i = 0; i < Dimension; ++i) { start[i] = 0; spacing[i] = testParameters.m_Spacing[i]; } size[0] = testParameters.m_ImageColumns; size[1] = testParameters.m_ImageRows; size[2] = testParameters.m_ImageSlices; MaskImageType::RegionType region; region.SetIndex(start); region.SetSize(size); mask->SetSpacing(spacing); mask->SetRegions(region); mask->Allocate(); typedef itk::ImageRegionIteratorWithIndex MaskImageIteratorType; MaskImageIteratorType maskIt(mask, region); for(maskIt.GoToBegin(); !maskIt.IsAtEnd(); ++maskIt) { maskIt.Set(0); } for(unsigned int i = 0; i < testParameters.m_NumberOfLabels; ++i) { for(maskIt.GoToBegin(); !maskIt.IsAtEnd(); ++maskIt) { MaskImageType::IndexType index = maskIt.GetIndex(); if((index[0] >= testParameters.m_MinIndexX[i] && index[0] <= testParameters.m_MaxIndexX[i] ) && (index[1] >= testParameters.m_MinIndexY[i] && index[1] <= testParameters.m_MaxIndexY[i] ) && (index[2] >= testParameters.m_MinIndexZ[i] && index[2] <= testParameters.m_MaxIndexZ[i] )) { maskIt.Set(testParameters.m_Label[i]); } } } mitk::CastToMitkImage(mask, mitkMaskImage); mitk::ImageMaskGenerator::Pointer imgMaskGen = mitk::ImageMaskGenerator::New(); imgMaskGen->SetImageMask(mitkMaskImage); mitk::HotspotMaskGenerator::Pointer hotspotMaskGen = mitk::HotspotMaskGenerator::New(); hotspotMaskGen->SetInputImage(image); hotspotMaskGen->SetLabel(testParameters.m_Label[label]); hotspotMaskGen->SetMask(imgMaskGen.GetPointer()); hotspotMaskGen->SetHotspotRadiusInMM(testParameters.m_HotspotRadiusInMM); if(testParameters.m_EntireHotspotInImage == 1) { MITK_INFO << "Hotspot must be completly inside image"; hotspotMaskGen->SetHotspotMustBeCompletelyInsideImage(true); } else { MITK_INFO << "Hotspot must not be completly inside image"; hotspotMaskGen->SetHotspotMustBeCompletelyInsideImage(false); } statisticsCalculator->SetMask(hotspotMaskGen.GetPointer()); MITK_DEBUG << "Masking is set to hotspot+image mask"; } else { mitk::HotspotMaskGenerator::Pointer hotspotMaskGen = mitk::HotspotMaskGenerator::New(); hotspotMaskGen->SetInputImage(image); hotspotMaskGen->SetHotspotRadiusInMM(testParameters.m_HotspotRadiusInMM); if(testParameters.m_EntireHotspotInImage == 1) { MITK_INFO << "Hotspot must be completly inside image"; hotspotMaskGen->SetHotspotMustBeCompletelyInsideImage(true); } else { MITK_INFO << "Hotspot must not be completly inside image"; hotspotMaskGen->SetHotspotMustBeCompletelyInsideImage(false); } MITK_DEBUG << "Masking is set to hotspot only"; } result = statisticsCalculator->GetStatistics(0); return result; } static void ValidateStatisticsItem(const std::string& label, double testvalue, double reference, double tolerance) { double diff = ::fabs(reference - testvalue); MITK_TEST_CONDITION( diff < tolerance, "'" << label << "' value close enough to reference value " "(value=" << testvalue << ", reference=" << reference << ", diff=" << diff << ")" ); } static void ValidateStatisticsItem(const std::string& label, const vnl_vector& testvalue, const vnl_vector& reference) { double diffX = ::fabs(double(testvalue[0] - reference[0])); double diffY = ::fabs(double(testvalue[1] - reference[1])); double diffZ = ::fabs(double(testvalue[2] - reference[2])); std::stringstream testPosition; testPosition << testvalue[0] << "," << testvalue[1] << "," << testvalue[2]; std::stringstream referencePosition; referencePosition << reference[0] << "," << reference[1] << "," << reference[2]; MITK_TEST_CONDITION( diffX < mitk::eps && diffY < mitk::eps && diffZ < mitk::eps, "'" << label << "' close enough to reference value " << "(value=[" << testPosition.str() << "]," << " reference=[" << referencePosition.str() << "]"); } /** \brief Compares calculated against actual statistics values. Checks validness of all statistics aspects. Lets test fail if any aspect is not sufficiently equal. */ - static void ValidateStatistics(const mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer hotspotStatistics, const Parameters& testParameters, unsigned int label) + static void ValidateStatistics(const mitk::StatisticsContainer::Pointer hotspotStatistics, const Parameters& testParameters, unsigned int label) { // check all expected test result against actual results double eps = 0.25; // value above the largest tested difference ValidateStatisticsItem("Hotspot mean", hotspotStatistics->GetMean(), testParameters.m_HotspotMean[label], eps); ValidateStatisticsItem("Hotspot maximum", hotspotStatistics->GetMax(), testParameters.m_HotspotMax[label], eps); ValidateStatisticsItem("Hotspot minimum", hotspotStatistics->GetMin(), testParameters.m_HotspotMin[label], eps); vnl_vector referenceHotspotCenterIndex; referenceHotspotCenterIndex.set_size(3); referenceHotspotCenterIndex[0] = testParameters.m_HotspotIndexX[label]; referenceHotspotCenterIndex[1] = testParameters.m_HotspotIndexY[label]; referenceHotspotCenterIndex[2] = testParameters.m_HotspotIndexZ[label]; // ValidateStatisticsItem("Hotspot center position", statistics.GetHotspotStatistics().GetHotspotIndex(), referenceHotspotCenterIndex); TODO: new image statistics calculator does not give hotspot position // TODO we do not test minimum/maximum positions within the peak/hotspot region, because // these positions are not unique, i.e. there are multiple valid minima/maxima positions. // One solution would be to modify the test cases in order to achive clear positions. // The BETTER/CORRECT solution would be to change the singular position into a set of positions / a region } }; /** \brief Verifies that hotspot statistics part of ImageStatisticsCalculator. The test reads parameters from an XML-file to generate a test-image, calculates the hotspot statistics of the image and checks if the calculated statistics are the same as the specified values of the XML-file. */ int mitkImageStatisticsHotspotTest(int argc, char* argv[]) { MITK_TEST_BEGIN("mitkImageStatisticsHotspotTest") try { mitkImageStatisticsHotspotTestClass::Parameters parameters = mitkImageStatisticsHotspotTestClass::ParseParameters(argc,argv); mitk::Image::Pointer image = mitkImageStatisticsHotspotTestClass::BuildTestImage(parameters); MITK_TEST_CONDITION_REQUIRED( image.IsNotNull(), "Generate test image" ); for(unsigned int label = 0; label < parameters.m_NumberOfLabels; ++label) { - mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer statistics = mitkImageStatisticsHotspotTestClass::CalculateStatistics(image, parameters, label); + mitk::StatisticsContainer::Pointer statistics = mitkImageStatisticsHotspotTestClass::CalculateStatistics(image, parameters, label); mitkImageStatisticsHotspotTestClass::ValidateStatistics(statistics, parameters, label); std::cout << std::endl; } } catch (std::exception& e) { std::cout << "Error: " << e.what() << std::endl; MITK_TEST_CONDITION_REQUIRED( false, "Exception occurred during test execution: " << e.what() ); } catch(...) { MITK_TEST_CONDITION_REQUIRED( false, "Exception occurred during test execution." ); } MITK_TEST_END() } diff --git a/Modules/ImageStatistics/files.cmake b/Modules/ImageStatistics/files.cmake index 5904c1fdfb..30c4ac9f67 100644 --- a/Modules/ImageStatistics/files.cmake +++ b/Modules/ImageStatistics/files.cmake @@ -1,33 +1,35 @@ set(CPP_FILES mitkImageStatisticsCalculator.cpp + mitkImageStatisticsContainer.cpp mitkPointSetStatisticsCalculator.cpp mitkPointSetDifferenceStatisticsCalculator.cpp mitkIntensityProfile.cpp mitkHotspotMaskGenerator.cpp mitkMaskGenerator.cpp mitkPlanarFigureMaskGenerator.cpp mitkMultiLabelMaskGenerator.cpp mitkImageMaskGenerator.cpp mitkHistogramStatisticsCalculator.cpp mitkMaskUtilities.cpp mitkIgnorePixelMaskGenerator.cpp ) set(H_FILES mitkImageStatisticsCalculator.h + mitkImageStatisticsContainer.h mitkPointSetDifferenceStatisticsCalculator.h mitkPointSetStatisticsCalculator.h mitkExtendedStatisticsImageFilter.h mitkExtendedLabelStatisticsImageFilter.h mitkHotspotMaskGenerator.h mitkMaskGenerator.h mitkPlanarFigureMaskGenerator.h mitkMultiLabelMaskGenerator.h mitkImageMaskGenerator.h mitkHistogramStatisticsCalculator.h mitkMaskUtilities.h mitkitkMaskImageFilter.h mitkIgnorePixelMaskGenerator.h mitkMinMaxImageFilterWithIndex.h mitkMinMaxLabelmageFilterWithIndex.h ) diff --git a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp index 3a5dc97fdd..21b2ad6415 100644 --- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp +++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp @@ -1,630 +1,502 @@ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "itkImageFileWriter.h" namespace mitk { void ImageStatisticsCalculator::SetInputImage(mitk::Image::Pointer image) { if (image != m_Image) { m_Image = image; m_StatisticsByTimeStep.resize(m_Image->GetTimeSteps()); m_StatisticsUpdateTimePerTimeStep.resize(m_Image->GetTimeSteps()); std::fill(m_StatisticsUpdateTimePerTimeStep.begin(), m_StatisticsUpdateTimePerTimeStep.end(), 0); this->Modified(); } } void ImageStatisticsCalculator::SetMask(mitk::MaskGenerator::Pointer mask) { if (mask != m_MaskGenerator) { m_MaskGenerator = mask; this->Modified(); } } void ImageStatisticsCalculator::SetSecondaryMask(mitk::MaskGenerator::Pointer mask) { if (mask != m_SecondaryMaskGenerator) { m_SecondaryMaskGenerator = mask; this->Modified(); } } void ImageStatisticsCalculator::SetNBinsForHistogramStatistics(unsigned int nBins) { if (nBins != m_nBinsForHistogramStatistics) { m_nBinsForHistogramStatistics = nBins; this->Modified(); this->m_UseBinSizeOverNBins = false; } if (m_UseBinSizeOverNBins) { this->Modified(); this->m_UseBinSizeOverNBins = false; } } unsigned int ImageStatisticsCalculator::GetNBinsForHistogramStatistics() const { return m_nBinsForHistogramStatistics; } void ImageStatisticsCalculator::SetBinSizeForHistogramStatistics(double binSize) { if (binSize != m_binSizeForHistogramStatistics) { m_binSizeForHistogramStatistics = binSize; this->Modified(); this->m_UseBinSizeOverNBins = true; } if (!m_UseBinSizeOverNBins) { this->Modified(); this->m_UseBinSizeOverNBins = true; } } double ImageStatisticsCalculator::GetBinSizeForHistogramStatistics() const { return m_binSizeForHistogramStatistics; } - ImageStatisticsCalculator::StatisticsContainer::Pointer ImageStatisticsCalculator::GetStatistics(unsigned int timeStep, unsigned int label) + mitk::StatisticsContainer::Pointer ImageStatisticsCalculator::GetStatistics(unsigned int timeStep, unsigned int label) { if (timeStep >= m_StatisticsByTimeStep.size()) { mitkThrow() << "invalid timeStep in ImageStatisticsCalculator_v2::GetStatistics"; } if (m_Image.IsNull()) { mitkThrow() << "no image"; } if (!m_Image->IsInitialized()) { mitkThrow() << "Image not initialized!"; } if (IsUpdateRequired(timeStep)) { if (m_MaskGenerator.IsNotNull()) { m_MaskGenerator->SetTimeStep(timeStep); m_InternalMask = m_MaskGenerator->GetMask(); if (m_MaskGenerator->GetReferenceImage().IsNotNull()) { m_InternalImageForStatistics = m_MaskGenerator->GetReferenceImage(); } else { m_InternalImageForStatistics = m_Image; } } else { m_InternalImageForStatistics = m_Image; } if (m_SecondaryMaskGenerator.IsNotNull()) { m_SecondaryMaskGenerator->SetTimeStep(timeStep); m_SecondaryMask = m_SecondaryMaskGenerator->GetMask(); } ImageTimeSelector::Pointer imgTimeSel = ImageTimeSelector::New(); imgTimeSel->SetInput(m_InternalImageForStatistics); imgTimeSel->SetTimeNr(timeStep); imgTimeSel->UpdateLargestPossibleRegion(); m_ImageTimeSlice = imgTimeSel->GetOutput(); // Calculate statistics with/without mask if (m_MaskGenerator.IsNull() && m_SecondaryMaskGenerator.IsNull()) { // 1) calculate statistics unmasked: AccessByItk_1(m_ImageTimeSlice, InternalCalculateStatisticsUnmasked, timeStep) } else { // 2) calculate statistics masked AccessByItk_1(m_ImageTimeSlice, InternalCalculateStatisticsMasked, timeStep) } //this->Modified(); } m_StatisticsUpdateTimePerTimeStep[timeStep] = m_StatisticsByTimeStep[timeStep][m_StatisticsByTimeStep[timeStep].size()-1]->GetMTime(); for (auto it = m_StatisticsByTimeStep[timeStep].begin(); it != m_StatisticsByTimeStep[timeStep].end(); ++it) { StatisticsContainer::Pointer statCont = *it; if (statCont->GetLabel() == label) { return statCont->Clone(); } } // these lines will ony be executed if the requested label could not be found! MITK_WARN << "Invalid label: " << label << " in time step: " << timeStep; return StatisticsContainer::New(); } template < typename TPixel, unsigned int VImageDimension > void ImageStatisticsCalculator::InternalCalculateStatisticsUnmasked( typename itk::Image< TPixel, VImageDimension >* image, unsigned int timeStep) { typedef typename itk::Image< TPixel, VImageDimension > ImageType; typedef typename itk::ExtendedStatisticsImageFilter ImageStatisticsFilterType; typedef typename itk::MinMaxImageFilterWithIndex MinMaxFilterType; StatisticsContainer::Pointer statisticsResult = StatisticsContainer::New(); typename ImageStatisticsFilterType::Pointer statisticsFilter = ImageStatisticsFilterType::New(); statisticsFilter->SetInput(image); statisticsFilter->SetCoordinateTolerance(0.001); statisticsFilter->SetDirectionTolerance(0.001); // TODO: this is single threaded. Implement our own image filter that does this multi threaded // typename itk::MinimumMaximumImageCalculator::Pointer imgMinMaxFilter = itk::MinimumMaximumImageCalculator::New(); // imgMinMaxFilter->SetImage(image); // imgMinMaxFilter->Compute(); vnl_vector minIndex, maxIndex; typename MinMaxFilterType::Pointer minMaxFilter = MinMaxFilterType::New(); minMaxFilter->SetInput(image); minMaxFilter->UpdateLargestPossibleRegion(); typename ImageType::PixelType minval = minMaxFilter->GetMin(); typename ImageType::PixelType maxval = minMaxFilter->GetMax(); typename ImageType::IndexType tmpMinIndex = minMaxFilter->GetMinIndex(); typename ImageType::IndexType tmpMaxIndex = minMaxFilter->GetMaxIndex(); // typename ImageType::IndexType tmpMinIndex = imgMinMaxFilter->GetIndexOfMinimum(); // typename ImageType::IndexType tmpMaxIndex = imgMinMaxFilter->GetIndexOfMaximum(); minIndex.set_size(tmpMaxIndex.GetIndexDimension()); maxIndex.set_size(tmpMaxIndex.GetIndexDimension()); for (unsigned int i=0; i < tmpMaxIndex.GetIndexDimension(); i++) { minIndex[i] = tmpMinIndex[i]; maxIndex[i] = tmpMaxIndex[i]; } statisticsResult->SetMinIndex(minIndex); statisticsResult->SetMaxIndex(maxIndex); //convert m_binSize in m_nBins if necessary unsigned int nBinsForHistogram; if (m_UseBinSizeOverNBins) { nBinsForHistogram = std::max(static_cast(std::ceil(maxval - minval)) / m_binSizeForHistogramStatistics, 10.); // do not allow less than 10 bins } else { nBinsForHistogram = m_nBinsForHistogramStatistics; } statisticsFilter->SetHistogramParameters(nBinsForHistogram, minval, maxval); try { statisticsFilter->Update(); } catch (const itk::ExceptionObject& e) { mitkThrow() << "Image statistics calculation failed due to following ITK Exception: \n " << e.what(); } // no mask, therefore just one label = the whole image m_StatisticsByTimeStep[timeStep].resize(1); statisticsResult->SetLabel(1); statisticsResult->SetN(image->GetLargestPossibleRegion().GetNumberOfPixels()); statisticsResult->SetMean(statisticsFilter->GetMean()); statisticsResult->SetMin(statisticsFilter->GetMinimum()); statisticsResult->SetMax(statisticsFilter->GetMaximum()); - statisticsResult->SetVariance(statisticsFilter->GetVariance()); statisticsResult->SetStd(statisticsFilter->GetSigma()); statisticsResult->SetSkewness(statisticsFilter->GetSkewness()); statisticsResult->SetKurtosis(statisticsFilter->GetKurtosis()); statisticsResult->SetRMS(std::sqrt(std::pow(statisticsFilter->GetMean(), 2.) + statisticsFilter->GetVariance())); // variance = sigma^2 statisticsResult->SetMPP(statisticsFilter->GetMPP()); statisticsResult->SetEntropy(statisticsFilter->GetEntropy()); statisticsResult->SetMedian(statisticsFilter->GetMedian()); statisticsResult->SetUniformity(statisticsFilter->GetUniformity()); statisticsResult->SetUPP(statisticsFilter->GetUPP()); statisticsResult->SetHistogram(statisticsFilter->GetHistogram()); m_StatisticsByTimeStep[timeStep][0] = statisticsResult; } template < typename TPixel, unsigned int VImageDimension > void ImageStatisticsCalculator::InternalCalculateStatisticsMasked( typename itk::Image< TPixel, VImageDimension >* image, unsigned int timeStep) { typedef itk::Image< TPixel, VImageDimension > ImageType; typedef itk::Image< MaskPixelType, VImageDimension > MaskType; typedef typename MaskType::PixelType LabelPixelType; typedef itk::ExtendedLabelStatisticsImageFilter< ImageType, MaskType > ImageStatisticsFilterType; typedef MaskUtilities< TPixel, VImageDimension > MaskUtilType; typedef typename itk::MinMaxLabelImageFilterWithIndex MinMaxLabelFilterType; typedef typename ImageType::PixelType InputImgPixelType; // workaround: if m_SecondaryMaskGenerator ist not null but m_MaskGenerator is! (this is the case if we request a 'ignore zuero valued pixels' // mask in the gui but do not define a primary mask) bool swapMasks = false; if (m_SecondaryMask.IsNotNull() && m_InternalMask.IsNull()) { m_InternalMask = m_SecondaryMask; m_SecondaryMask = nullptr; swapMasks = true; } // maskImage has to have the same dimension as image typename MaskType::Pointer maskImage = MaskType::New(); try { // try to access the pixel values directly (no copying or casting). Only works if mask pixels are of pixelType unsigned short maskImage = ImageToItkImage< MaskPixelType, VImageDimension >(m_InternalMask); } catch (const itk::ExceptionObject &) { // if the pixel type of the mask is not short, then we have to make a copy of m_InternalMask (and cast the values) CastToItkImage(m_InternalMask, maskImage); } // if we have a secondary mask (say a ignoreZeroPixelMask) we need to combine the masks (corresponds to AND) if (m_SecondaryMask.IsNotNull()) { // dirty workaround for a bug when pf mask + any other mask is used in conjunction. We need a proper fix for this (Fabian Isensee is responsible and probably working on it!) if (m_InternalMask->GetDimension() == 2 && (m_SecondaryMask->GetDimension() == 3 || m_SecondaryMask->GetDimension() == 4)) { mitk::Image::Pointer old_img = m_SecondaryMaskGenerator->GetReferenceImage(); m_SecondaryMaskGenerator->SetInputImage(m_MaskGenerator->GetReferenceImage()); m_SecondaryMask = m_SecondaryMaskGenerator->GetMask(); m_SecondaryMaskGenerator->SetInputImage(old_img); } typename MaskType::Pointer secondaryMaskImage = MaskType::New(); secondaryMaskImage = ImageToItkImage< MaskPixelType, VImageDimension >(m_SecondaryMask); // secondary mask should be a ignore zero value pixel mask derived from image. it has to be cropped to the mask region (which may be planar or simply smaller) typename MaskUtilities::Pointer secondaryMaskMaskUtil = MaskUtilities::New(); secondaryMaskMaskUtil->SetImage(secondaryMaskImage.GetPointer()); secondaryMaskMaskUtil->SetMask(maskImage.GetPointer()); typename MaskType::Pointer adaptedSecondaryMaskImage = secondaryMaskMaskUtil->ExtractMaskImageRegion(); typename itk::MaskImageFilter2::Pointer maskFilter = itk::MaskImageFilter2::New(); maskFilter->SetInput1(maskImage); maskFilter->SetInput2(adaptedSecondaryMaskImage); maskFilter->SetMaskingValue(1); // all pixels of maskImage where secondaryMaskImage==1 will be kept, all the others are set to 0 maskFilter->UpdateLargestPossibleRegion(); maskImage = maskFilter->GetOutput(); } typename MaskUtilType::Pointer maskUtil = MaskUtilType::New(); maskUtil->SetImage(image); maskUtil->SetMask(maskImage.GetPointer()); // if mask is smaller than image, extract the image region where the mask is typename ImageType::Pointer adaptedImage = ImageType::New(); adaptedImage = maskUtil->ExtractMaskImageRegion(); // this also checks mask sanity // find min, max, minindex and maxindex typename MinMaxLabelFilterType::Pointer minMaxFilter = MinMaxLabelFilterType::New(); minMaxFilter->SetInput(adaptedImage); minMaxFilter->SetLabelInput(maskImage); minMaxFilter->UpdateLargestPossibleRegion(); // set histogram parameters for each label individually (min/max may be different for each label) typedef typename std::map MapType; typedef typename std::pair PairType; std::vector relevantLabels = minMaxFilter->GetRelevantLabels(); MapType minVals; MapType maxVals; std::map nBins; for (LabelPixelType label:relevantLabels) { minVals.insert(PairType(label, minMaxFilter->GetMin(label))); maxVals.insert(PairType(label, minMaxFilter->GetMax(label))); unsigned int nBinsForHistogram; if (m_UseBinSizeOverNBins) { nBinsForHistogram = std::max(static_cast(std::ceil(minMaxFilter->GetMax(label) - minMaxFilter->GetMin(label))) / m_binSizeForHistogramStatistics, 10.); // do not allow less than 10 bins } else { nBinsForHistogram = m_nBinsForHistogramStatistics; } nBins.insert(typename std::pair(label, nBinsForHistogram)); } typename ImageStatisticsFilterType::Pointer imageStatisticsFilter = ImageStatisticsFilterType::New(); imageStatisticsFilter->SetDirectionTolerance(0.001); imageStatisticsFilter->SetCoordinateTolerance(0.001); imageStatisticsFilter->SetInput(adaptedImage); imageStatisticsFilter->SetLabelInput(maskImage); imageStatisticsFilter->SetHistogramParametersForLabels(nBins, minVals, maxVals); imageStatisticsFilter->Update(); std::list labels = imageStatisticsFilter->GetRelevantLabels(); auto it = labels.begin(); m_StatisticsByTimeStep[timeStep].resize(0); while(it != labels.end()) { StatisticsContainer::Pointer statisticsResult = StatisticsContainer::New(); // find min, max, minindex and maxindex // make sure to only look in the masked region, use a masker for this vnl_vector minIndex, maxIndex; mitk::Point3D worldCoordinateMin; mitk::Point3D worldCoordinateMax; mitk::Point3D indexCoordinateMin; mitk::Point3D indexCoordinateMax; m_InternalImageForStatistics->GetGeometry()->IndexToWorld(minMaxFilter->GetMinIndex(*it), worldCoordinateMin); m_InternalImageForStatistics->GetGeometry()->IndexToWorld(minMaxFilter->GetMaxIndex(*it), worldCoordinateMax); m_Image->GetGeometry()->WorldToIndex(worldCoordinateMin, indexCoordinateMin); m_Image->GetGeometry()->WorldToIndex(worldCoordinateMax, indexCoordinateMax); minIndex.set_size(3); maxIndex.set_size(3); //for (unsigned int i=0; i < tmpMaxIndex.GetIndexDimension(); i++) for (unsigned int i=0; i < 3; i++) { minIndex[i] = indexCoordinateMin[i]; maxIndex[i] = indexCoordinateMax[i]; } statisticsResult->SetMinIndex(minIndex); statisticsResult->SetMaxIndex(maxIndex); assert(std::abs(minMaxFilter->GetMax(*it) - imageStatisticsFilter->GetMaximum(*it)) < mitk::eps); assert(std::abs(minMaxFilter->GetMin(*it) - imageStatisticsFilter->GetMinimum(*it)) < mitk::eps); statisticsResult->SetN(imageStatisticsFilter->GetSum(*it) / (double) imageStatisticsFilter->GetMean(*it)); statisticsResult->SetMean(imageStatisticsFilter->GetMean(*it)); statisticsResult->SetMin(imageStatisticsFilter->GetMinimum(*it)); statisticsResult->SetMax(imageStatisticsFilter->GetMaximum(*it)); - statisticsResult->SetVariance(imageStatisticsFilter->GetVariance(*it)); statisticsResult->SetStd(imageStatisticsFilter->GetSigma(*it)); statisticsResult->SetSkewness(imageStatisticsFilter->GetSkewness(*it)); statisticsResult->SetKurtosis(imageStatisticsFilter->GetKurtosis(*it)); statisticsResult->SetRMS(std::sqrt(std::pow(imageStatisticsFilter->GetMean(*it), 2.) + imageStatisticsFilter->GetVariance(*it))); // variance = sigma^2 statisticsResult->SetMPP(imageStatisticsFilter->GetMPP(*it)); statisticsResult->SetLabel(*it); statisticsResult->SetEntropy(imageStatisticsFilter->GetEntropy(*it)); statisticsResult->SetMedian(imageStatisticsFilter->GetMedian(*it)); statisticsResult->SetUniformity(imageStatisticsFilter->GetUniformity(*it)); statisticsResult->SetUPP(imageStatisticsFilter->GetUPP(*it)); statisticsResult->SetHistogram(imageStatisticsFilter->GetHistogram(*it)); m_StatisticsByTimeStep[timeStep].push_back(statisticsResult); ++it; } // swap maskGenerators back if (swapMasks) { m_SecondaryMask = m_InternalMask; m_InternalMask = nullptr; } } bool ImageStatisticsCalculator::IsUpdateRequired(unsigned int timeStep) const { unsigned long thisClassTimeStamp = this->GetMTime(); unsigned long inputImageTimeStamp = m_Image->GetMTime(); unsigned long statisticsTimeStamp = m_StatisticsUpdateTimePerTimeStep[timeStep]; if (thisClassTimeStamp > statisticsTimeStamp) // inputs have changed { return true; } if (inputImageTimeStamp > statisticsTimeStamp) // image has changed { return true; } if (m_MaskGenerator.IsNotNull()) { unsigned long maskGeneratorTimeStamp = m_MaskGenerator->GetMTime(); if (maskGeneratorTimeStamp > statisticsTimeStamp) // there is a mask generator and it has changed { return true; } } if (m_SecondaryMaskGenerator.IsNotNull()) { unsigned long maskGeneratorTimeStamp = m_SecondaryMaskGenerator->GetMTime(); if (maskGeneratorTimeStamp > statisticsTimeStamp) // there is a secondary mask generator and it has changed { return true; } } return false; } - - - ImageStatisticsCalculator::StatisticsContainer::StatisticsContainer(): - m_N(0), - m_Mean(nan("")), - m_Min(nan("")), - m_Max(nan("")), - m_Std(nan("")), - m_Variance(nan("")), - m_Skewness(nan("")), - m_Kurtosis(nan("")), - m_RMS(nan("")), - m_MPP(nan("")), - m_Median(nan("")), - m_Uniformity(nan("")), - m_UPP(nan("")), - m_Entropy(nan("")), - m_Label(0) - { - m_minIndex.set_size(0); - m_maxIndex.set_size(0); - } - - ImageStatisticsCalculator::statisticsMapType ImageStatisticsCalculator::StatisticsContainer::GetStatisticsAsMap() - { - ImageStatisticsCalculator::statisticsMapType statisticsAsMap; - - statisticsAsMap["N"] = m_N; - statisticsAsMap["Mean"] = m_Mean; - statisticsAsMap["Min"] = m_Min; - statisticsAsMap["Max"] = m_Max; - statisticsAsMap["StandardDeviation"] = m_Std; - statisticsAsMap["Variance"] = m_Variance; - statisticsAsMap["Skewness"] = m_Skewness; - statisticsAsMap["Kurtosis"] = m_Kurtosis; - statisticsAsMap["RMS"] = m_RMS; - statisticsAsMap["MPP"] = m_MPP; - statisticsAsMap["Median"] = m_Median; - statisticsAsMap["Uniformity"] = m_Uniformity; - statisticsAsMap["UPP"] = m_UPP; - statisticsAsMap["Entropy"] = m_Entropy; - statisticsAsMap["Label"] = m_Label; - - return statisticsAsMap; - } - - - void ImageStatisticsCalculator::StatisticsContainer::Reset() - { - m_N = 0; - m_Mean = nan(""); - m_Min = nan(""); - m_Max = nan(""); - m_Std = nan(""); - m_Variance = nan(""); - m_Skewness = nan(""); - m_Kurtosis = nan(""); - m_RMS = nan(""); - m_MPP = nan(""); - m_Median = nan(""); - m_Uniformity = nan(""); - m_UPP = nan(""); - m_Entropy = nan(""); - m_Histogram = HistogramType::New(); - m_minIndex.set_size(0); - m_maxIndex.set_size(0); - m_Label = 0; - } - - void ImageStatisticsCalculator::StatisticsContainer::Print() - { - ImageStatisticsCalculator::statisticsMapType statMap = this->GetStatisticsAsMap(); - // print all map key value pairs - // const auto& val:statMap - for (auto it = statMap.begin(); it != statMap.end(); ++it) - { - std::cout << it->first << ": " << it->second << std::endl; - } - - // print the min and max index - std::cout << "Min Index:" << std::endl; - for (auto it = this->GetMinIndex().begin(); it != this->GetMinIndex().end(); ++it) - { - std::cout << *it << " "; - } - std::cout << std::endl; - - // print the min and max index - std::cout << "Max Index:" << std::endl; - for (auto it = this->GetMaxIndex().begin(); it != this->GetMaxIndex().end(); ++it) - { - std::cout << *it << " "; - } - std::cout << std::endl; - } - - std::string ImageStatisticsCalculator::StatisticsContainer::GetAsString() - { - std::string res = ""; - ImageStatisticsCalculator::statisticsMapType statMap = this->GetStatisticsAsMap(); - // print all map key value pairs - // const auto& val:statMap - for (auto it = statMap.begin(); it != statMap.end(); ++it) - { - res += std::string(it->first) + ": " + std::to_string(it->second) + "\n"; - } - - // print the min and max index - res += "Min Index:" + std::string("\n"); - for (auto it = this->GetMinIndex().begin(); it != this->GetMinIndex().end(); it++) - { - res += std::to_string(*it) + std::string(" "); - } - res += "\n"; - - // print the min and max index - res += "Max Index:" + std::string("\n"); - for (auto it = this->GetMaxIndex().begin(); it != this->GetMaxIndex().end(); it++) - { - res += std::to_string(*it) + " "; - } - res += "\n"; - return res; - } - - } diff --git a/Modules/ImageStatistics/mitkImageStatisticsCalculator.h b/Modules/ImageStatistics/mitkImageStatisticsCalculator.h index 7a8f23403c..63117b1c82 100644 --- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.h +++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.h @@ -1,414 +1,121 @@ #ifndef MITKIMAGESTATISTICSCALCULATOR #define MITKIMAGESTATISTICSCALCULATOR #include #include #include +#include #include #include #include #include namespace mitk { class MITKIMAGESTATISTICS_EXPORT ImageStatisticsCalculator: public itk::Object { public: /** Standard Self typedef */ typedef ImageStatisticsCalculator Self; typedef itk::Object Superclass; typedef itk::SmartPointer< Self > Pointer; typedef itk::SmartPointer< const Self > ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self) /** Runtime information support. */ itkTypeMacro(ImageStatisticsCalculator_v2, itk::Object) typedef double statisticsValueType; typedef std::map statisticsMapType; typedef itk::Statistics::Histogram HistogramType; typedef unsigned short MaskPixelType; - /**Documentation - @brief Container class for storing the computed image statistics. - - Container class for storing the computed image statistics. Stored statistics are: - - N: number of voxels - - Mean - - MPP (Mean of positive pixels) - - Median - - Skewness - - Kurtosis - - Uniformity - - UPP (Uniformity of positive pixels) - - Variance - - Std (Standard Deviation) - - Min - - Max - - RMS (Root Mean Square) - - Label (if applicable, the label (unsigned short) of the mask the statistics belong to) - - Entropy - - It furthermore stores the following: - - MinIndex (Index of Image where the Minimum is located) - - MaxIndex (Index of Image where the Maximum is located) - - Histogram of Pixel Values*/ - class MITKIMAGESTATISTICS_EXPORT StatisticsContainer : public itk::Object - { - public: - /** Standard Self typedef */ - typedef StatisticsContainer Self; - typedef itk::Object Superclass; - typedef itk::SmartPointer< Self > Pointer; - typedef itk::SmartPointer< const Self > ConstPointer; - - /** Method for creation through the object factory. */ - itkNewMacro(Self) - - /** Runtime information support. */ - itkTypeMacro(StatisticsContainer, itk::Object) - - typedef double RealType; - - /**Documentation - @brief Returns a std::map containing all real valued statistics stored in this class (= all statistics except minIndex, maxIndex and the histogram)*/ - statisticsMapType GetStatisticsAsMap(); - - /**Documentation - @brief Deletes all stored values*/ - void Reset(); - - void SetN(long n) - { - m_N = n; - } - - const long& GetN() const - { - return m_N; - } - - void SetMean(RealType mean) - { - m_Mean = mean; - } - - const RealType& GetMean() const - { - return m_Mean; - } - - void SetVariance(RealType variance) - { - m_Variance = variance; - } - - const RealType& GetVariance() const - { - return m_Variance; - } - - void SetStd(RealType std) - { - m_Std = std; - } - - const RealType& GetStd() const - { - return m_Std; - } - - void SetMin(RealType minVal) - { - m_Min = minVal; - } - - const RealType& GetMin() const - { - return m_Min; - } - - void SetMax(RealType maxVal) - { - m_Max = maxVal; - } - - const RealType& GetMax() const - { - return m_Max; - } - - void SetRMS(RealType rms) - { - m_RMS = rms; - } - - const RealType& GetRMS() const - { - return m_RMS; - } - - void SetSkewness(RealType skewness) - { - m_Skewness = skewness; - } - - const RealType& GetSkewness() const - { - return m_Skewness; - } - - void SetKurtosis(RealType kurtosis) - { - m_Kurtosis = kurtosis; - } - - const RealType& GetKurtosis() const - { - return m_Kurtosis; - } - - void SetMPP(RealType mpp) - { - m_MPP = mpp; - } - - const RealType& GetMPP() const - { - return m_MPP; - } - - void SetLabel(unsigned int label) - { - m_Label = label; - } - - const unsigned int& GetLabel() const - { - return m_Label; - } - - void SetMinIndex(vnl_vector minIndex) - { - m_minIndex = minIndex; - } - - vnl_vector GetMinIndex() const - { - return m_minIndex; - } - - void SetMaxIndex(vnl_vector maxIndex) - { - m_maxIndex = maxIndex; - } - - vnl_vector GetMaxIndex() const - { - return m_maxIndex; - } - - void SetHistogram(HistogramType::Pointer hist) - { - if (m_Histogram != hist) - { - m_Histogram = hist; - } - } - - const HistogramType::Pointer GetHistogram() const - { - return m_Histogram; - } - - void SetEntropy(RealType entropy) - { - m_Entropy = entropy; - } - - const RealType & GetEntropy() const - { - return m_Entropy; - } - - void SetMedian(RealType median) - { - m_Median = median; - } - - const RealType & GetMedian() const - { - return m_Median; - } - - void SetUniformity(RealType uniformity) - { - m_Uniformity = uniformity; - } - - const RealType & GetUniformity() const - { - return m_Uniformity; - } - - void SetUPP(RealType upp) - { - m_UPP = upp; - } - - const RealType & GetUPP() const - { - return m_UPP; - } - - /**Documentation - @brief Creates a StatisticsMapType containing all real valued statistics stored in this class (= all statistics except minIndex, maxIndex and the histogram) and prints its contents to std::cout*/ - void Print(); - - /**Documentation - @brief Generates a string that contains all real valued statistics stored in this class (= all statistics except minIndex, maxIndex and the histogram)*/ - std::string GetAsString(); - - - protected: - StatisticsContainer(); - - private: - itk::LightObject::Pointer InternalClone() const override - { - itk::LightObject::Pointer ioPtr = Superclass::InternalClone(); - Self::Pointer rval = dynamic_cast(ioPtr.GetPointer()); - if (rval.IsNull()) - { - itkExceptionMacro(<< "downcast to type " - << "StatisticsContainer" - << " failed."); - } - - rval->SetEntropy(this->GetEntropy()); - rval->SetKurtosis(this->GetKurtosis()); - rval->SetLabel(this->GetLabel()); - rval->SetMax(this->GetMax()); - rval->SetMin(this->GetMin()); - rval->SetMean(this->GetMean()); - rval->SetMedian(this->GetMedian()); - rval->SetMPP(this->GetMPP()); - rval->SetN(this->GetN()); - rval->SetRMS(this->GetRMS()); - rval->SetSkewness(this->GetSkewness()); - rval->SetStd(this->GetStd()); - rval->SetUniformity(this->GetUniformity()); - rval->SetUPP(this->GetUPP()); - rval->SetVariance(this->GetVariance()); - rval->SetHistogram(this->GetHistogram()); - rval->SetMinIndex(this->GetMinIndex()); - rval->SetMaxIndex(this->GetMaxIndex()); - return ioPtr; - } - - // not pretty, is temporary - long m_N; - RealType m_Mean, m_Min, m_Max, m_Std, m_Variance; - RealType m_Skewness; - RealType m_Kurtosis; - RealType m_RMS; - RealType m_MPP; - vnl_vector m_minIndex, m_maxIndex; - RealType m_Median; - RealType m_Uniformity; - RealType m_UPP; - RealType m_Entropy; - unsigned int m_Label; - HistogramType::Pointer m_Histogram; - - }; - /**Documentation @brief Set the image for which the statistics are to be computed.*/ void SetInputImage(mitk::Image::Pointer image); /**Documentation @brief Set the mask generator that creates the mask which is to be used to calculate statistics. If no more mask is desired simply set @param mask to nullptr*/ void SetMask(mitk::MaskGenerator::Pointer mask); /**Documentation @brief Set this if more than one mask should be applied (for instance if a IgnorePixelValueMask were to be used alongside with a segmentation). Both masks are combined using pixel wise AND operation. The secondary mask does not have to be the same size than the primary but they need to have some overlap*/ void SetSecondaryMask(mitk::MaskGenerator::Pointer mask); /**Documentation @brief Set number of bins to be used for histogram statistics. If Bin size is set after number of bins, bin size will be used instead!*/ void SetNBinsForHistogramStatistics(unsigned int nBins); /**Documentation @brief Retrieve the number of bins used for histogram statistics. Careful: The return value does not indicate whether NBins or BinSize is used. That solely depends on which parameter has been set last.*/ unsigned int GetNBinsForHistogramStatistics() const; /**Documentation @brief Set bin size to be used for histogram statistics. If nbins is set after bin size, nbins will be used instead!*/ void SetBinSizeForHistogramStatistics(double binSize); /**Documentation @brief Retrieve the bin size for histogram statistics. Careful: The return value does not indicate whether NBins or BinSize is used. That solely depends on which parameter has been set last.*/ double GetBinSizeForHistogramStatistics() const; /**Documentation @brief Returns the statistics for label @a label and timeStep @a timeStep. If these requested statistics are not computed yet the computation is done as well. For performance reasons, statistics for all labels in the image are computed at once. */ StatisticsContainer::Pointer GetStatistics(unsigned int timeStep=0, unsigned int label=1); protected: ImageStatisticsCalculator(){ m_nBinsForHistogramStatistics = 100; m_binSizeForHistogramStatistics = 10; m_UseBinSizeOverNBins = false; }; private: template < typename TPixel, unsigned int VImageDimension > void InternalCalculateStatisticsUnmasked( typename itk::Image< TPixel, VImageDimension >* image, unsigned int timeStep); template < typename TPixel, unsigned int VImageDimension > typename HistogramType::Pointer InternalCalculateHistogramUnmasked( typename itk::Image< TPixel, VImageDimension >* image, double minVal, double maxVal); template < typename TPixel, unsigned int VImageDimension > void InternalCalculateStatisticsMasked( typename itk::Image< TPixel, VImageDimension >* image, unsigned int timeStep); bool IsUpdateRequired(unsigned int timeStep) const; std::string GetNameOfClass() { return std::string("ImageStatisticsCalculator_v2"); } mitk::Image::Pointer m_Image; mitk::Image::Pointer m_ImageTimeSlice; mitk::Image::Pointer m_InternalImageForStatistics; mitk::MaskGenerator::Pointer m_MaskGenerator; mitk::Image::Pointer m_InternalMask; mitk::MaskGenerator::Pointer m_SecondaryMaskGenerator; mitk::Image::Pointer m_SecondaryMask; unsigned int m_nBinsForHistogramStatistics; double m_binSizeForHistogramStatistics; bool m_UseBinSizeOverNBins; std::vector> m_StatisticsByTimeStep; std::vector m_StatisticsUpdateTimePerTimeStep; }; } #endif // MITKIMAGESTATISTICSCALCULATOR diff --git a/Modules/ImageStatistics/mitkImageStatisticsContainer.cpp b/Modules/ImageStatistics/mitkImageStatisticsContainer.cpp new file mode 100644 index 0000000000..949352f326 --- /dev/null +++ b/Modules/ImageStatistics/mitkImageStatisticsContainer.cpp @@ -0,0 +1,166 @@ +/*=================================================================== + +The Medical Imaging Interaction Toolkit (MITK) + +Copyright (c) German Cancer Research Center, +Division of Medical and Biological Informatics. +All rights reserved. + +This software is distributed WITHOUT ANY WARRANTY; without +even the implied warranty of MERCHANTABILITY or FITNESS FOR +A PARTICULAR PURPOSE. + +See LICENSE.txt or http://www.mitk.org for details. + +===================================================================*/ +#include + +namespace mitk +{ + StatisticsContainer::StatisticsContainer(): + m_N(0), + m_Mean(nan("")), + m_Min(nan("")), + m_Max(nan("")), + m_Std(nan("")), + m_Skewness(nan("")), + m_Kurtosis(nan("")), + m_RMS(nan("")), + m_MPP(nan("")), + m_Median(nan("")), + m_Uniformity(nan("")), + m_UPP(nan("")), + m_Entropy(nan("")), + m_Label(0) + { + m_MinIndex.set_size(0); + m_MaxIndex.set_size(0); + } + + StatisticsContainer::RealType StatisticsContainer::GetVariance() const + { + return m_Std * m_Std; + } + + void StatisticsContainer::SetHistogram(HistogramType::Pointer hist) + { + if (m_Histogram != hist) + { + m_Histogram = hist; + } + } + + void StatisticsContainer::PrintSelf(std::ostream &os, itk::Indent indent) const + { + Superclass::PrintSelf(os, indent); + auto statisticsMap = GetStatisticsAsMap(); + os << std::endl << indent << "Statistics instance:"; + for (const auto& aStatisticValue : statisticsMap) { + os << std::endl << indent.GetNextIndent() << aStatisticValue.first << ": " << aStatisticValue.second; + } + } + + StatisticsContainer::statisticsMapType StatisticsContainer::GetStatisticsAsMap() const + { + statisticsMapType statisticsAsMap; + + statisticsAsMap["N"] = m_N; + statisticsAsMap["Mean"] = m_Mean; + statisticsAsMap["Min"] = m_Min; + statisticsAsMap["Max"] = m_Max; + statisticsAsMap["StandardDeviation"] = m_Std; + statisticsAsMap["Skewness"] = m_Skewness; + statisticsAsMap["Kurtosis"] = m_Kurtosis; + statisticsAsMap["RMS"] = m_RMS; + statisticsAsMap["MPP"] = m_MPP; + statisticsAsMap["Median"] = m_Median; + statisticsAsMap["Uniformity"] = m_Uniformity; + statisticsAsMap["UPP"] = m_UPP; + statisticsAsMap["Entropy"] = m_Entropy; + statisticsAsMap["Label"] = m_Label; + + return statisticsAsMap; + } + + + void StatisticsContainer::Reset() + { + m_N = 0; + m_Mean = nan(""); + m_Min = nan(""); + m_Max = nan(""); + m_Std = nan(""); + m_Skewness = nan(""); + m_Kurtosis = nan(""); + m_RMS = nan(""); + m_MPP = nan(""); + m_Median = nan(""); + m_Uniformity = nan(""); + m_UPP = nan(""); + m_Entropy = nan(""); + m_Histogram = HistogramType::New(); + m_MinIndex.set_size(0); + m_MaxIndex.set_size(0); + m_Label = 0; + } + + itk::LightObject::Pointer StatisticsContainer::InternalClone() const + { + itk::LightObject::Pointer ioPtr = Superclass::InternalClone(); + Self::Pointer rval = dynamic_cast(ioPtr.GetPointer()); + if (rval.IsNull()) + { + itkExceptionMacro(<< "downcast to type " + << "StatisticsContainer" + << " failed."); + } + + rval->SetEntropy(this->GetEntropy()); + rval->SetKurtosis(this->GetKurtosis()); + rval->SetLabel(this->GetLabel()); + rval->SetMax(this->GetMax()); + rval->SetMin(this->GetMin()); + rval->SetMean(this->GetMean()); + rval->SetMedian(this->GetMedian()); + rval->SetMPP(this->GetMPP()); + rval->SetN(this->GetN()); + rval->SetRMS(this->GetRMS()); + rval->SetSkewness(this->GetSkewness()); + rval->SetStd(this->GetStd()); + rval->SetUniformity(this->GetUniformity()); + rval->SetUPP(this->GetUPP()); + rval->SetHistogram(this->GetHistogram()); + rval->SetMinIndex(this->GetMinIndex()); + rval->SetMaxIndex(this->GetMaxIndex()); + return ioPtr; + } + + void StatisticsContainer::Print() + { + statisticsMapType statMap = this->GetStatisticsAsMap(); + // print all map key value pairs + // const auto& val:statMap + for (auto it = statMap.begin(); it != statMap.end(); ++it) + { + std::cout << it->first << ": " << it->second << std::endl; + } + + // print the min and max index + std::cout << "Min Index:" << std::endl; + for (auto it = this->GetMinIndex().begin(); it != this->GetMinIndex().end(); ++it) + { + std::cout << *it << " "; + } + std::cout << std::endl; + + // print the min and max index + std::cout << "Max Index:" << std::endl; + for (auto it = this->GetMaxIndex().begin(); it != this->GetMaxIndex().end(); ++it) + { + std::cout << *it << " "; + } + std::cout << std::endl; + } + + +} diff --git a/Modules/ImageStatistics/mitkImageStatisticsContainer.h b/Modules/ImageStatistics/mitkImageStatisticsContainer.h new file mode 100644 index 0000000000..41a14d7bb7 --- /dev/null +++ b/Modules/ImageStatistics/mitkImageStatisticsContainer.h @@ -0,0 +1,158 @@ +/*=================================================================== + +The Medical Imaging Interaction Toolkit (MITK) + +Copyright (c) German Cancer Research Center, +Division of Medical and Biological Informatics. +All rights reserved. + +This software is distributed WITHOUT ANY WARRANTY; without +even the implied warranty of MERCHANTABILITY or FITNESS FOR +A PARTICULAR PURPOSE. + +See LICENSE.txt or http://www.mitk.org for details. + +===================================================================*/ + +#ifndef MITKIMAGESTATISTICSCONTAINER +#define MITKIMAGESTATISTICSCONTAINER + +#include +#include +#include + +namespace mitk +{ + /**Documentation + @brief Container class for storing the computed image statistics. + + Container class for storing the computed image statistics. Stored statistics are: + - N: number of voxels + - Mean + - MPP (Mean of positive pixels) + - Median + - Skewness + - Kurtosis + - Uniformity + - UPP (Uniformity of positive pixels) + - Std (Standard Deviation) + - Min + - Max + - RMS (Root Mean Square) + - Label (if applicable, the label (unsigned short) of the mask the statistics belong to) + - Entropy + + It furthermore stores the following: + - MinIndex (Index of Image where the Minimum is located) + - MaxIndex (Index of Image where the Maximum is located) + - Histogram of Pixel Values*/ + class MITKIMAGESTATISTICS_EXPORT StatisticsContainer : public mitk::BaseData + { + public: + mitkClassMacro(StatisticsContainer, mitk::BaseData) + + /** Method for creation through the object factory. */ + itkNewMacro(Self) + + typedef itk::Statistics::Histogram HistogramType; + typedef double RealType; + typedef std::map statisticsMapType; + + virtual void SetRequestedRegionToLargestPossibleRegion() override {}; + + virtual bool RequestedRegionIsOutsideOfTheBufferedRegion() override { return false; }; + + virtual bool VerifyRequestedRegion() override { return true; }; + + virtual void SetRequestedRegion(const itk::DataObject*) override {}; + + /**Documentation + @brief Returns a std::map containing all real valued statistics stored in this class (= all statistics except minIndex, maxIndex and the histogram)*/ + statisticsMapType GetStatisticsAsMap() const; + + /**Documentation + @brief Deletes all stored values*/ + void Reset(); + + itkSetMacro(N, long); + itkGetConstMacro(N, long); + + itkSetMacro(Mean, RealType); + itkGetConstMacro(Mean, RealType); + + itkSetMacro(Std, RealType); + itkGetConstMacro(Std, RealType); + + itkSetMacro(Min, RealType); + itkGetConstMacro(Min, RealType); + + itkSetMacro(Max, RealType); + itkGetConstMacro(Max, RealType); + + itkSetMacro(RMS, RealType); + itkGetConstMacro(RMS, RealType); + + RealType GetVariance() const; + + itkSetMacro(Skewness, RealType); + itkGetConstMacro(Skewness, RealType); + + itkSetMacro(Kurtosis, RealType); + itkGetConstMacro(Kurtosis, RealType); + + itkSetMacro(MPP, RealType); + itkGetConstMacro(MPP, RealType); + + itkSetMacro(Label, unsigned int); + itkGetConstMacro(Label, unsigned int); + + itkSetMacro(MinIndex, vnl_vector); + itkGetConstMacro(MinIndex, vnl_vector); + + itkSetMacro(MaxIndex, vnl_vector); + itkGetConstMacro(MaxIndex, vnl_vector); + + void SetHistogram(HistogramType::Pointer hist); + + itkGetConstMacro(Histogram, HistogramType::Pointer); + + itkSetMacro(Entropy, RealType); + itkGetConstMacro(Entropy, RealType); + + itkSetMacro(Median, RealType); + itkGetConstMacro(Median, RealType); + + itkSetMacro(Uniformity, RealType); + itkGetConstMacro(Uniformity, RealType); + + itkSetMacro(UPP, RealType); + itkGetConstMacro(UPP, RealType); + + /**Documentation + @brief Creates a StatisticsMapType containing all real valued statistics stored in this class (= all statistics except minIndex, maxIndex and the histogram) and prints its contents to std::cout*/ + void Print(); + + protected: + StatisticsContainer(); + virtual void PrintSelf(std::ostream &os, itk::Indent indent) const override; + + private: + itk::LightObject::Pointer InternalClone() const override; + + long m_N; + RealType m_Mean, m_Min, m_Max, m_Std; + RealType m_Skewness; + RealType m_Kurtosis; + RealType m_RMS; + RealType m_MPP; + vnl_vector m_MinIndex, m_MaxIndex; + RealType m_Median; + RealType m_Uniformity; + RealType m_UPP; + RealType m_Entropy; + unsigned int m_Label; + HistogramType::Pointer m_Histogram; + }; +} +#endif // MITKIMAGESTATISTICSCONTAINER + diff --git a/Modules/ImageStatistics/mitkIntensityProfile.cpp b/Modules/ImageStatistics/mitkIntensityProfile.cpp index 0a75482d2e..d2fbb9eedf 100644 --- a/Modules/ImageStatistics/mitkIntensityProfile.cpp +++ b/Modules/ImageStatistics/mitkIntensityProfile.cpp @@ -1,380 +1,380 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include #include #include #include #include #include #include #include "mitkIntensityProfile.h" using namespace mitk; template static void ReadPixel(const PixelType&, Image::Pointer image, const itk::Index<3>& index, ScalarType* returnValue) { switch (image->GetDimension()) { case 2: { ImagePixelReadAccessor readAccess(image, image->GetSliceData(0)); *returnValue = readAccess.GetPixelByIndex(reinterpret_cast&>(index)); break; } case 3: { ImagePixelReadAccessor readAccess(image, image->GetVolumeData(0)); *returnValue = readAccess.GetPixelByIndex(index); break; } default: *returnValue = 0; break; } } static IntensityProfile::Pointer ComputeIntensityProfile(Image::Pointer image, itk::PolyLineParametricPath<3>::Pointer path) { IntensityProfile::Pointer intensityProfile = IntensityProfile::New(); itk::PolyLineParametricPath<3>::InputType input = path->StartOfInput(); BaseGeometry* imageGeometry = image->GetGeometry(); const PixelType pixelType = image->GetPixelType(); IntensityProfile::MeasurementVectorType measurementVector; itk::PolyLineParametricPath<3>::OffsetType offset; Point3D worldPoint; itk::Index<3> index; do { imageGeometry->IndexToWorld(path->Evaluate(input), worldPoint); imageGeometry->WorldToIndex(worldPoint, index); mitkPixelTypeMultiplex3(ReadPixel, pixelType, image, index, measurementVector.GetDataPointer()); intensityProfile->PushBack(measurementVector); offset = path->IncrementInput(input); } while ((offset[0] | offset[1] | offset[2]) != 0); return intensityProfile; } template static typename itk::InterpolateImageFunction::Pointer CreateInterpolateImageFunction(InterpolateImageFunction::Enum interpolator) { switch (interpolator) { case InterpolateImageFunction::NearestNeighbor: return itk::NearestNeighborInterpolateImageFunction::New().GetPointer(); case InterpolateImageFunction::Linear: return itk::LinearInterpolateImageFunction::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Blackman_3: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Blackman_4: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Blackman_5: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Cosine_3: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Cosine_4: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Cosine_5: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Hamming_3: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Hamming_4: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Hamming_5: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Lanczos_3: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Lanczos_4: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Lanczos_5: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Welch_3: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Welch_4: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); case InterpolateImageFunction::WindowedSinc_Welch_5: return itk::WindowedSincInterpolateImageFunction >::New().GetPointer(); default: return itk::NearestNeighborInterpolateImageFunction::New().GetPointer(); } } template static void ComputeIntensityProfile(itk::Image* image, itk::PolyLineParametricPath<3>::Pointer path, unsigned int numSamples, InterpolateImageFunction::Enum interpolator, IntensityProfile::Pointer intensityProfile) { typename itk::InterpolateImageFunction >::Pointer interpolateImageFunction = CreateInterpolateImageFunction >(interpolator); interpolateImageFunction->SetInputImage(image); const itk::PolyLineParametricPath<3>::InputType startOfInput = path->StartOfInput(); const itk::PolyLineParametricPath<3>::InputType delta = 1.0 / (numSamples - 1); IntensityProfile::MeasurementVectorType measurementVector; for (unsigned int i = 0; i < numSamples; ++i) { measurementVector[0] = interpolateImageFunction->EvaluateAtContinuousIndex(path->Evaluate(startOfInput + i * delta)); intensityProfile->PushBack(measurementVector); } } static IntensityProfile::Pointer ComputeIntensityProfile(Image::Pointer image, itk::PolyLineParametricPath<3>::Pointer path, unsigned int numSamples, InterpolateImageFunction::Enum interpolator) { IntensityProfile::Pointer intensityProfile = IntensityProfile::New(); AccessFixedDimensionByItk_n(image, ComputeIntensityProfile, 3, (path, numSamples, interpolator, intensityProfile)); return intensityProfile; } class AddPolyLineElementToPath { public: AddPolyLineElementToPath(const PlaneGeometry* planarFigureGeometry, const BaseGeometry* imageGeometry, itk::PolyLineParametricPath<3>::Pointer path) : m_PlanarFigureGeometry(planarFigureGeometry), m_ImageGeometry(imageGeometry), m_Path(path) { } void operator()(const PlanarFigure::PolyLineElement& polyLineElement) { m_PlanarFigureGeometry->Map(polyLineElement, m_WorldPoint); m_ImageGeometry->WorldToIndex(m_WorldPoint, m_ContinuousIndexPoint); m_Vertex.CastFrom(m_ContinuousIndexPoint); m_Path->AddVertex(m_Vertex); } private: const PlaneGeometry* m_PlanarFigureGeometry; const BaseGeometry* m_ImageGeometry; itk::PolyLineParametricPath<3>::Pointer m_Path; Point3D m_WorldPoint; Point3D m_ContinuousIndexPoint; itk::PolyLineParametricPath<3>::ContinuousIndexType m_Vertex; }; static itk::PolyLineParametricPath<3>::Pointer CreatePathFromPlanarFigure(BaseGeometry* imageGeometry, PlanarFigure* planarFigure) { itk::PolyLineParametricPath<3>::Pointer path = itk::PolyLineParametricPath<3>::New(); const PlanarFigure::PolyLineType polyLine = planarFigure->GetPolyLine(0); std::for_each(polyLine.begin(), polyLine.end(), AddPolyLineElementToPath(planarFigure->GetPlaneGeometry(), imageGeometry, path)); return path; } static void AddPointToPath(const BaseGeometry* imageGeometry, const Point3D& point, itk::PolyLineParametricPath<3>::Pointer path) { Point3D continuousIndexPoint; imageGeometry->WorldToIndex(point, continuousIndexPoint); itk::PolyLineParametricPath<3>::ContinuousIndexType vertex; vertex.CastFrom(continuousIndexPoint); path->AddVertex(vertex); } static itk::PolyLineParametricPath<3>::Pointer CreatePathFromPoints(BaseGeometry* imageGeometry, const Point3D& startPoint, const Point3D& endPoint) { itk::PolyLineParametricPath<3>::Pointer path = itk::PolyLineParametricPath<3>::New(); AddPointToPath(imageGeometry, startPoint, path); AddPointToPath(imageGeometry, endPoint, path); return path; } IntensityProfile::Pointer mitk::ComputeIntensityProfile(Image::Pointer image, PlanarFigure::Pointer planarFigure) { return ::ComputeIntensityProfile(image, CreatePathFromPlanarFigure(image->GetGeometry(), planarFigure)); } IntensityProfile::Pointer mitk::ComputeIntensityProfile(Image::Pointer image, PlanarLine::Pointer planarLine, unsigned int numSamples, InterpolateImageFunction::Enum interpolator) { return ::ComputeIntensityProfile(image, CreatePathFromPlanarFigure(image->GetGeometry(), planarLine.GetPointer()), numSamples, interpolator); } IntensityProfile::Pointer mitk::ComputeIntensityProfile(Image::Pointer image, const Point3D& startPoint, const Point3D& endPoint, unsigned int numSamples, InterpolateImageFunction::Enum interpolator) { return ::ComputeIntensityProfile(image, CreatePathFromPoints(image->GetGeometry(), startPoint, endPoint), numSamples, interpolator); } IntensityProfile::InstanceIdentifier mitk::ComputeGlobalMaximum(IntensityProfile::ConstPointer intensityProfile, IntensityProfile::MeasurementType &max) { max = -vcl_numeric_limits::min(); IntensityProfile::InstanceIdentifier maxIndex = 0; IntensityProfile::ConstIterator end = intensityProfile->End(); IntensityProfile::MeasurementType measurement; for (IntensityProfile::ConstIterator it = intensityProfile->Begin(); it != end; ++it) { measurement = it.GetMeasurementVector()[0]; if (measurement > max) { max = measurement; maxIndex = it.GetInstanceIdentifier(); } } return maxIndex; } IntensityProfile::InstanceIdentifier mitk::ComputeGlobalMinimum(IntensityProfile::ConstPointer intensityProfile, IntensityProfile::MeasurementType &min) { min = vcl_numeric_limits::max(); IntensityProfile::InstanceIdentifier minIndex = 0; IntensityProfile::ConstIterator end = intensityProfile->End(); IntensityProfile::MeasurementType measurement; for (IntensityProfile::ConstIterator it = intensityProfile->Begin(); it != end; ++it) { measurement = it.GetMeasurementVector()[0]; if (measurement < min) { min = measurement; minIndex = it.GetInstanceIdentifier(); } } return minIndex; } IntensityProfile::InstanceIdentifier mitk::ComputeCenterOfMaximumArea(IntensityProfile::ConstPointer intensityProfile, IntensityProfile::InstanceIdentifier radius) { //const IntensityProfile::MeasurementType min = intensityProfile->GetMeasurementVector(ComputeGlobalMinimum(intensityProfile))[0]; IntensityProfile::MeasurementType min; ComputeGlobalMinimum(intensityProfile, min); const IntensityProfile::InstanceIdentifier areaWidth = 1 + 2 * radius; IntensityProfile::MeasurementType maxArea = 0; for (IntensityProfile::InstanceIdentifier i = 0; i < areaWidth; ++i) maxArea += intensityProfile->GetMeasurementVector(i)[0] - min; const IntensityProfile::InstanceIdentifier lastIndex = intensityProfile->Size() - areaWidth; IntensityProfile::InstanceIdentifier centerOfMaxArea = radius; IntensityProfile::MeasurementType area = maxArea; for (IntensityProfile::InstanceIdentifier i = 1; i <= lastIndex; ++i) { area += intensityProfile->GetMeasurementVector(i + areaWidth - 1)[0] - min; area -= intensityProfile->GetMeasurementVector(i - 1)[0] - min; if (area > maxArea) { maxArea = area; centerOfMaxArea = i + radius; // TODO: If multiple areas in the neighborhood have the same intensity chose the middle one instead of the first one. } } return centerOfMaxArea; } std::vector mitk::CreateVectorFromIntensityProfile(IntensityProfile::ConstPointer intensityProfile) { std::vector result; result.reserve(intensityProfile->Size()); IntensityProfile::ConstIterator end = intensityProfile->End(); for (IntensityProfile::ConstIterator it = intensityProfile->Begin(); it != end; ++it) result.push_back(it.GetMeasurementVector()[0]); return result; } IntensityProfile::Pointer mitk::CreateIntensityProfileFromVector(const std::vector& vector) { const IntensityProfile::InstanceIdentifier size = vector.size(); IntensityProfile::Pointer result = IntensityProfile::New(); result->Resize(size); for (IntensityProfile::InstanceIdentifier i = 0; i < size; ++i) result->SetMeasurement(i, 0, vector[i]); return result; } -void mitk::ComputeIntensityProfileStatistics(IntensityProfile::ConstPointer intensityProfile, ImageStatisticsCalculator::StatisticsContainer::Pointer stats) +void mitk::ComputeIntensityProfileStatistics(IntensityProfile::ConstPointer intensityProfile, StatisticsContainer::Pointer stats) { typedef std::vector StatsVecType; StatsVecType statsVec = mitk::CreateVectorFromIntensityProfile( intensityProfile ); IntensityProfile::MeasurementType min; IntensityProfile::MeasurementType max; mitk::ComputeGlobalMinimum( intensityProfile, min ); mitk::ComputeGlobalMaximum( intensityProfile, max ); StatsVecType::size_type numSamples = statsVec.size(); double mean = 0.0; double rms = 0.0; for ( StatsVecType::const_iterator it = statsVec.begin(); it != statsVec.end(); ++it ) { double val = *it; mean += val; rms += val*val; } mean /= numSamples; rms /= numSamples; double var = 0.0; for ( StatsVecType::const_iterator it = statsVec.begin(); it != statsVec.end(); ++it ) { double diff = *it - mean; var += diff*diff; } var /= ( numSamples - 1 ); rms = sqrt( rms ); stats->SetMin( static_cast( min ) ); stats->SetMax( static_cast( max ) ); stats->SetN( numSamples ); stats->SetMean( mean ); - stats->SetVariance( var ); + stats->SetStd( sqrt(var) ); stats->SetRMS( rms ); } diff --git a/Modules/ImageStatistics/mitkIntensityProfile.h b/Modules/ImageStatistics/mitkIntensityProfile.h index 95022ab617..cc8e3ce4e3 100644 --- a/Modules/ImageStatistics/mitkIntensityProfile.h +++ b/Modules/ImageStatistics/mitkIntensityProfile.h @@ -1,137 +1,137 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef mitkIntensityProfile_h #define mitkIntensityProfile_h #include #include #include #include #include namespace mitk { typedef itk::Statistics::ListSample::MeasurementVectorType> IntensityProfile; /** \brief Compute intensity profile of an image for each pixel along the first PolyLine of a given planar figure. * * \param[in] image A two or three-dimensional image which consists of single component pixels. * \param[in] planarFigure A planar figure from which the first PolyLine is used to evaluate the intensity profile. * * \return The computed intensity profile. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::Pointer ComputeIntensityProfile(Image::Pointer image, PlanarFigure::Pointer planarFigure); namespace InterpolateImageFunction { enum Enum { NearestNeighbor, Linear, WindowedSinc_Blackman_3, WindowedSinc_Blackman_4, WindowedSinc_Blackman_5, WindowedSinc_Cosine_3, WindowedSinc_Cosine_4, WindowedSinc_Cosine_5, WindowedSinc_Hamming_3, WindowedSinc_Hamming_4, WindowedSinc_Hamming_5, WindowedSinc_Lanczos_3, WindowedSinc_Lanczos_4, WindowedSinc_Lanczos_5, WindowedSinc_Welch_3, WindowedSinc_Welch_4, WindowedSinc_Welch_5 }; } /** \brief Compute intensity profile of an image for each sample along a planar line. * * \param[in] image A three-dimensional image which consists of single component pixels. * \param[in] planarLine A planar line along which the intensity profile will be evaluated. * \param[in] numSamples Number of samples along the planar line (must be at least 2). * \param[in] interpolator Image interpolation function which is used to read each sample. * * \return The computed intensity profile. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::Pointer ComputeIntensityProfile(Image::Pointer image, PlanarLine::Pointer planarLine, unsigned int numSamples, InterpolateImageFunction::Enum interpolator = InterpolateImageFunction::NearestNeighbor); /** \brief Compute intensity profile of an image for each sample between two points. * * \param[in] image A three-dimensional image which consists of single component pixels. * \param[in] startPoint A point at which the first sample is to be read. * \param[in] endPoint A point at which the last sample is to be read. * \param[in] numSamples Number of samples between startPoint and endPoint (must be at least 2). * \param[in] interpolator Image interpolation function which is used to read each sample. * * \return The computed intensity profile. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::Pointer ComputeIntensityProfile(Image::Pointer image, const Point3D& startPoint, const Point3D& endPoint, unsigned int numSamples, InterpolateImageFunction::Enum interpolator = InterpolateImageFunction::NearestNeighbor); /** \brief Compute global maximum of an intensity profile. * * \param[in] intensityProfile An intensity profile. * * \return Index of the global maximum. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::InstanceIdentifier ComputeGlobalMaximum(IntensityProfile::ConstPointer intensityProfile, IntensityProfile::MeasurementType &max); /** \brief Compute global minimum of an intensity profile. * * \param[in] intensityProfile An intensity profile. * * \return Index of the global minimum. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::InstanceIdentifier ComputeGlobalMinimum(IntensityProfile::ConstPointer intensityProfile, IntensityProfile::MeasurementType &min); /** \brief Compute statistics of an intensity profile. * * \param[in] intensityProfile An intensity profile. * * \param[in] stats An ImageStatisticsCalculator::Statistics object to hold the calculated statistics. * */ - MITKIMAGESTATISTICS_EXPORT void ComputeIntensityProfileStatistics(IntensityProfile::ConstPointer intensityProfile, ImageStatisticsCalculator::StatisticsContainer::Pointer stats); + MITKIMAGESTATISTICS_EXPORT void ComputeIntensityProfileStatistics(IntensityProfile::ConstPointer intensityProfile, StatisticsContainer::Pointer stats); /** \brief Compute center of maximum area under the curve of an intensity profile. * * \param[in] intensityProfile An intensity profile. * \param[in] radius Radius of the area (width of area equals 1 + 2 * radius). * * \return Index of the maximum area center. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::InstanceIdentifier ComputeCenterOfMaximumArea(IntensityProfile::ConstPointer intensityProfile, IntensityProfile::InstanceIdentifier radius); /** \brief Convert an intensity profile to a standard library vector. * * \param[in] intensityProfile An intensity profile. * * \return Standard library vector which contains the input intensity profile measurements. */ MITKIMAGESTATISTICS_EXPORT std::vector CreateVectorFromIntensityProfile(IntensityProfile::ConstPointer intensityProfile); /** \brief Convert a standard library vector to an intensity profile. * * \param[in] vector An standard library vector which contains intensity profile measurements. * * \return An intensity profile. */ MITKIMAGESTATISTICS_EXPORT IntensityProfile::Pointer CreateIntensityProfileFromVector(const std::vector& vector); } #endif diff --git a/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.cpp b/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.cpp index 221b97c69e..4b724d0788 100644 --- a/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.cpp +++ b/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.cpp @@ -1,216 +1,215 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkPointSetDifferenceStatisticsCalculator.h" mitk::PointSetDifferenceStatisticsCalculator::PointSetDifferenceStatisticsCalculator() : m_StatisticsCalculated(false) { - m_Statistics = ImageStatisticsCalculator::StatisticsContainer::New(); + m_Statistics = StatisticsContainer::New(); m_PointSet1 = mitk::PointSet::New(); m_PointSet2 = mitk::PointSet::New(); //m_Statistics.Reset(); } mitk::PointSetDifferenceStatisticsCalculator::PointSetDifferenceStatisticsCalculator(mitk::PointSet::Pointer pSet1, mitk::PointSet::Pointer pSet2) { - m_Statistics = ImageStatisticsCalculator::StatisticsContainer::New(); + m_Statistics = StatisticsContainer::New(); m_PointSet1 = pSet1; m_PointSet2 = pSet2; m_StatisticsCalculated = false; //m_Statistics.Reset(); } mitk::PointSetDifferenceStatisticsCalculator::~PointSetDifferenceStatisticsCalculator() { } void mitk::PointSetDifferenceStatisticsCalculator::SetPointSets(mitk::PointSet::Pointer pSet1, mitk::PointSet::Pointer pSet2) { if (pSet1.IsNotNull()) { m_PointSet1 = pSet1; } if (pSet2.IsNotNull()) { m_PointSet2 = pSet2; } m_StatisticsCalculated = false; //m_Statistics.Reset(); } std::vector mitk::PointSetDifferenceStatisticsCalculator::GetDifferences() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_DifferencesVector; } std::vector mitk::PointSetDifferenceStatisticsCalculator::GetSquaredDifferences() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_SquaredDifferencesVector; } double mitk::PointSetDifferenceStatisticsCalculator::GetMean() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetMean(); } double mitk::PointSetDifferenceStatisticsCalculator::GetSD() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetStd(); } double mitk::PointSetDifferenceStatisticsCalculator::GetVariance() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetVariance(); } double mitk::PointSetDifferenceStatisticsCalculator::GetRMS() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetRMS(); } double mitk::PointSetDifferenceStatisticsCalculator::GetMedian() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetMedian(); } double mitk::PointSetDifferenceStatisticsCalculator::GetMax() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetMax(); } double mitk::PointSetDifferenceStatisticsCalculator::GetMin() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetMin(); } double mitk::PointSetDifferenceStatisticsCalculator::GetNumberOfPoints() { if (!m_StatisticsCalculated) { this->ComputeStatistics(); } return m_Statistics->GetN(); } void mitk::PointSetDifferenceStatisticsCalculator::ComputeStatistics() { if ((m_PointSet1.IsNull())||(m_PointSet2.IsNull())) { itkExceptionMacro("Point sets specified are not valid. Please specify correct Point sets"); } else if (m_PointSet1->GetSize()!=m_PointSet2->GetSize()) { itkExceptionMacro("PointSets are not equal. Please make sure that your PointSets have the same size and hold corresponding points."); } else if (m_PointSet1->GetSize()==0) { itkExceptionMacro("There are no points in the PointSets. Please make sure that the PointSets contain points"); } else { double mean = 0.0; double sd = 0.0; double rms= 0.0; std::vector differencesVector; mitk::Point3D point1; mitk::Point3D point2; int numberOfPoints = m_PointSet1->GetSize(); //Iterate over both pointsets in order to compare all points pair-wise mitk::PointSet::PointsIterator end = m_PointSet1->End(); for( mitk::PointSet::PointsIterator pointSetIterator = m_PointSet1->Begin(), pointSetIterator2 = m_PointSet2->Begin(); pointSetIterator != end; ++pointSetIterator, ++pointSetIterator2) //iterate simultaneously over both sets { point1 = pointSetIterator.Value(); point2 = pointSetIterator2.Value(); double squaredDistance = point1.SquaredEuclideanDistanceTo(point2); mean+=sqrt(squaredDistance); rms+=squaredDistance; this->m_SquaredDifferencesVector.push_back(squaredDistance); differencesVector.push_back(sqrt(squaredDistance)); } m_DifferencesVector = differencesVector; mean = mean/numberOfPoints; rms = sqrt(rms/numberOfPoints); for (std::vector::size_type i=0; iSetMean(mean); m_Statistics->SetStd(sd); - m_Statistics->SetVariance(variance); m_Statistics->SetRMS(rms); m_Statistics->SetMin(differencesVector.at(0)); m_Statistics->SetMax(differencesVector.at(numberOfPoints-1)); m_Statistics->SetMedian(median); m_Statistics->SetN(numberOfPoints); m_StatisticsCalculated = true; } } diff --git a/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.h b/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.h index 5f26224dde..46e3635937 100644 --- a/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.h +++ b/Modules/ImageStatistics/mitkPointSetDifferenceStatisticsCalculator.h @@ -1,110 +1,110 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef _MITK_PointSetDifferenceStatisticsCalculator_H #define _MITK_PointSetDifferenceStatisticsCalculator_H #include #include #include "mitkImageStatisticsCalculator.h" #include namespace mitk { /** * \brief Class for calculating the difference between two corresponding point sets. * The user can access the single distances between corresponding points as well as a complete statistic (mean, sd, rms, median, max, min) * The point sets must be of equal size! */ class MITKIMAGESTATISTICS_EXPORT PointSetDifferenceStatisticsCalculator : public itk::Object { public: mitkClassMacroItkParent( PointSetDifferenceStatisticsCalculator, itk::Object ); itkFactorylessNewMacro(Self) itkCloneMacro(Self) mitkNewMacro2Param(PointSetDifferenceStatisticsCalculator,mitk::PointSet::Pointer,mitk::PointSet::Pointer); /*! \brief set point sets to be compared */ void SetPointSets(mitk::PointSet::Pointer pSet1, mitk::PointSet::Pointer pSet2); /*! \brief returns a vector holding the differences between the corresponding points of the point sets */ std::vector GetDifferences(); /*! \brief returns a vector holding the squared differences between the corresponding points of the point sets */ std::vector GetSquaredDifferences(); /*! \brief returns the mean distance of all corresponding points of the point sets */ double GetMean(); /*! \brief returns the standard deviation of the distances between all corresponding points of the point sets */ double GetSD(); /*! \brief returns the variance of the distances between all corresponding points of the point sets */ double GetVariance(); /*! \brief returns the root mean squared distance of all corresponding points of the point sets */ double GetRMS(); /*! \brief returns the median distance of all corresponding points of the point sets */ double GetMedian(); /*! \brief returns the maximal distance of all corresponding points of the point sets */ double GetMax(); /*! \brief returns the minimal distance of all corresponding points of the point sets */ double GetMin(); /*! \brief returns the total number of corresponding points of the point sets */ double GetNumberOfPoints(); protected: PointSetDifferenceStatisticsCalculator(); PointSetDifferenceStatisticsCalculator(mitk::PointSet::Pointer,mitk::PointSet::Pointer); ~PointSetDifferenceStatisticsCalculator() override; /*! \brief Method for computing the complete statistics of the differences between the given point sets. */ void ComputeStatistics(); - mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer m_Statistics; ///< struct holding the statistics + mitk::StatisticsContainer::Pointer m_Statistics; ///< struct holding the statistics std::vector m_DifferencesVector; ///< vector holding the differences between the corresponding points std::vector m_SquaredDifferencesVector; ///< vector holding the squared differences between the corresponding points mitk::PointSet::Pointer m_PointSet1; ///< first point set used for comparison mitk::PointSet::Pointer m_PointSet2; ///< second point set used for comparison bool m_StatisticsCalculated; ///< flag indicating whether statistics are already calculated or not. }; } #endif // #define _MITK_PointSetDifferenceStatisticsCalculator_H diff --git a/Modules/Segmentation/Algorithms/mitkFeatureBasedEdgeDetectionFilter.cpp b/Modules/Segmentation/Algorithms/mitkFeatureBasedEdgeDetectionFilter.cpp index 623e1b17cc..f8875e48d2 100644 --- a/Modules/Segmentation/Algorithms/mitkFeatureBasedEdgeDetectionFilter.cpp +++ b/Modules/Segmentation/Algorithms/mitkFeatureBasedEdgeDetectionFilter.cpp @@ -1,198 +1,198 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkFeatureBasedEdgeDetectionFilter.h" #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include mitk::FeatureBasedEdgeDetectionFilter::FeatureBasedEdgeDetectionFilter() { this->SetNumberOfRequiredInputs(1); this->SetNumberOfIndexedOutputs(1); } mitk::FeatureBasedEdgeDetectionFilter::~FeatureBasedEdgeDetectionFilter() { } void mitk::FeatureBasedEdgeDetectionFilter::GenerateData() { mitk::Image::Pointer image = ImageToUnstructuredGridFilter::GetInput(); if (m_SegmentationMask.IsNull()) { MITK_WARN << "Please set a segmentation mask first" << std::endl; return; } // First create a threshold segmentation of the image. The threshold is determined // by the mean +/- stddev of the pixel values that are covered by the segmentation mask // Compute mean and stdDev based on the current segmentation mitk::ImageStatisticsCalculator::Pointer statCalc = mitk::ImageStatisticsCalculator::New(); statCalc->SetInputImage(image); mitk::ImageMaskGenerator::Pointer imgMask = mitk::ImageMaskGenerator::New(); imgMask->SetImageMask(m_SegmentationMask); - mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats = statCalc->GetStatistics(); + auto stats = statCalc->GetStatistics(); double mean = stats->GetMean(); double stdDev = stats->GetStd(); double upperThreshold = mean + stdDev; double lowerThreshold = mean - stdDev; // Perform thresholding mitk::Image::Pointer thresholdImage = mitk::Image::New(); AccessByItk_3(image.GetPointer(), ITKThresholding, lowerThreshold, upperThreshold, thresholdImage) mitk::ProgressBar::GetInstance() ->Progress(2); // Postprocess threshold segmentation // First a closing will be executed mitk::Image::Pointer closedImage = mitk::Image::New(); AccessByItk_1(thresholdImage, ThreadedClosing, closedImage); // Then we will holes that might exist mitk::MorphologicalOperations::FillHoles(closedImage); mitk::ProgressBar::GetInstance()->Progress(); // Extract the binary edges of the resulting segmentation mitk::Image::Pointer edgeImage = mitk::Image::New(); AccessByItk_1(closedImage, ContourSearch, edgeImage); // Convert the edge image into an unstructured grid mitk::ImageToUnstructuredGridFilter::Pointer i2UFilter = mitk::ImageToUnstructuredGridFilter::New(); i2UFilter->SetInput(edgeImage); i2UFilter->SetThreshold(1.0); i2UFilter->Update(); m_PointGrid = this->GetOutput(); if (m_PointGrid.IsNull()) m_PointGrid = mitk::UnstructuredGrid::New(); m_PointGrid->SetVtkUnstructuredGrid(i2UFilter->GetOutput()->GetVtkUnstructuredGrid()); mitk::ProgressBar::GetInstance()->Progress(); } template void mitk::FeatureBasedEdgeDetectionFilter::ThreadedClosing(itk::Image *originalImage, mitk::Image::Pointer &result) { typedef itk::BinaryBallStructuringElement myKernelType; myKernelType ball; ball.SetRadius(1); ball.CreateStructuringElement(); typedef typename itk::Image ImageType; typename itk::DilateObjectMorphologyImageFilter::Pointer dilationFilter = itk::DilateObjectMorphologyImageFilter::New(); dilationFilter->SetInput(originalImage); dilationFilter->SetKernel(ball); dilationFilter->Update(); typename itk::Image::Pointer dilatedImage = dilationFilter->GetOutput(); typename itk::ErodeObjectMorphologyImageFilter::Pointer erodeFilter = itk::ErodeObjectMorphologyImageFilter::New(); erodeFilter->SetInput(dilatedImage); erodeFilter->SetKernel(ball); erodeFilter->Update(); mitk::GrabItkImageMemory(erodeFilter->GetOutput(), result); } template void mitk::FeatureBasedEdgeDetectionFilter::ContourSearch(itk::Image *originalImage, mitk::Image::Pointer &result) { typedef itk::Image ImageType; typedef itk::BinaryContourImageFilter binaryContourImageFilterType; typename binaryContourImageFilterType::Pointer binaryContourFilter = binaryContourImageFilterType::New(); binaryContourFilter->SetInput(originalImage); binaryContourFilter->SetForegroundValue(1); binaryContourFilter->SetBackgroundValue(0); binaryContourFilter->Update(); typename itk::Image::Pointer itkImage = itk::Image::New(); itkImage->Graft(binaryContourFilter->GetOutput()); mitk::GrabItkImageMemory(itkImage, result); } template void mitk::FeatureBasedEdgeDetectionFilter::ITKThresholding(itk::Image *originalImage, double lower, double upper, mitk::Image::Pointer &result) { typedef itk::Image ImageType; typedef itk::Image SegmentationType; typedef itk::BinaryThresholdImageFilter ThresholdFilterType; if (typeid(TPixel) != typeid(float) && typeid(TPixel) != typeid(double)) { // round the thresholds if we have nor a float or double image lower = std::floor(lower + 0.5); upper = std::floor(upper - 0.5); } if (lower >= upper) { upper = lower; } typename ThresholdFilterType::Pointer filter = ThresholdFilterType::New(); filter->SetInput(originalImage); filter->SetLowerThreshold(lower); filter->SetUpperThreshold(upper); filter->SetInsideValue(1); filter->SetOutsideValue(0); filter->Update(); mitk::GrabItkImageMemory(filter->GetOutput(), result); } void mitk::FeatureBasedEdgeDetectionFilter::SetSegmentationMask(mitk::Image::Pointer segmentation) { this->m_SegmentationMask = segmentation; } void mitk::FeatureBasedEdgeDetectionFilter::GenerateOutputInformation() { Superclass::GenerateOutputInformation(); } diff --git a/Modules/SurfaceInterpolation/mitkImageToPointCloudFilter.cpp b/Modules/SurfaceInterpolation/mitkImageToPointCloudFilter.cpp index 235f5a9972..9b03408148 100644 --- a/Modules/SurfaceInterpolation/mitkImageToPointCloudFilter.cpp +++ b/Modules/SurfaceInterpolation/mitkImageToPointCloudFilter.cpp @@ -1,166 +1,166 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkImageToPointCloudFilter.h" #include #include #include #include #include #include #include #include #include mitk::ImageToPointCloudFilter::ImageToPointCloudFilter() : m_NumberOfExtractedPoints(0) { m_Method = DetectionMethod(0); this->SetNumberOfRequiredInputs(1); this->SetNumberOfIndexedOutputs(1); } mitk::ImageToPointCloudFilter::~ImageToPointCloudFilter() { } void mitk::ImageToPointCloudFilter::GenerateData() { mitk::Image::ConstPointer image = ImageToUnstructuredGridFilter::GetInput(); m_Geometry = image->GetGeometry(); if (image.IsNull()) { MITK_ERROR << "mitk::ImageToContourFilter: No input available. " "Please set the input!" << std::endl; return; } mitk::Image::Pointer notConstImage = const_cast(image.GetPointer()); switch (m_Method) { case 0: AccessByItk_1(notConstImage.GetPointer(), StdDeviations, 2) break; case 1: AccessByItk_1(notConstImage.GetPointer(), StdDeviations, 3) break; case 2: AccessByItk_1(notConstImage.GetPointer(), StdDeviations, 4) break; default: AccessByItk_1(notConstImage.GetPointer(), StdDeviations, 2) break; } } template void mitk::ImageToPointCloudFilter::StdDeviations(itk::Image *image, int amount) { typedef itk::Image InputImageType; typedef itk::CastImageFilter ImagePTypeToFloatPTypeCasterType; typedef itk::LaplacianImageFilter LaplacianFilterType; typename LaplacianFilterType::Pointer lapFilter = LaplacianFilterType::New(); typename ImagePTypeToFloatPTypeCasterType::Pointer caster = ImagePTypeToFloatPTypeCasterType::New(); caster->SetInput(image); caster->Update(); FloatImageType::Pointer fImage = caster->GetOutput(); lapFilter->SetInput(fImage); lapFilter->UpdateLargestPossibleRegion(); mitk::Image::Pointer edgeImage = mitk::ImportItkImage(lapFilter->GetOutput()); mitk::ImageStatisticsCalculator::Pointer statCalc = mitk::ImageStatisticsCalculator::New(); statCalc->SetInputImage(edgeImage); - mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats = statCalc->GetStatistics(); + auto stats = statCalc->GetStatistics(); double mean = stats->GetMean(); double stdDev = stats->GetStd(); double upperThreshold = mean + stdDev * amount; double lowerThreshold = mean - stdDev * amount; typename itk::ImageRegionIterator it(lapFilter->GetOutput(), lapFilter->GetOutput()->GetRequestedRegion()); vtkSmartPointer points = vtkSmartPointer::New(); double greatX = 0, greatY = 0, greatZ = 0; it.GoToBegin(); while (!it.IsAtEnd()) { if (it.Get() > lowerThreshold && it.Get() < upperThreshold) { it.Set(0); } else { it.Set(1); mitk::Point3D imagePoint; mitk::Point3D worldPoint; imagePoint[0] = it.GetIndex()[0]; imagePoint[1] = it.GetIndex()[1]; imagePoint[2] = it.GetIndex()[2]; m_Geometry->IndexToWorld(imagePoint, worldPoint); if (worldPoint[0] > greatX) greatX = worldPoint[0]; if (worldPoint[1] > greatY) greatY = worldPoint[1]; if (worldPoint[2] > greatZ) greatZ = worldPoint[2]; points->InsertNextPoint(worldPoint[0], worldPoint[1], worldPoint[2]); m_NumberOfExtractedPoints++; } ++it; } /*need to build the UnstructuredGrid with at least one vertex otherwise its not visible*/ vtkSmartPointer verts = vtkSmartPointer::New(); verts->GetPointIds()->SetNumberOfIds(m_NumberOfExtractedPoints); for (int i = 0; i < m_NumberOfExtractedPoints; i++) { verts->GetPointIds()->SetId(i, i); } vtkSmartPointer uGrid = vtkSmartPointer::New(); uGrid->Allocate(1); uGrid->InsertNextCell(verts->GetCellType(), verts->GetPointIds()); uGrid->SetPoints(points); mitk::UnstructuredGrid::Pointer outputGrid = mitk::UnstructuredGrid::New(); outputGrid->SetVtkUnstructuredGrid(uGrid); this->SetNthOutput(0, outputGrid); } void mitk::ImageToPointCloudFilter::GenerateOutputInformation() { Superclass::GenerateOutputInformation(); } diff --git a/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.cpp b/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.cpp index c065e8d218..b26e18d811 100644 --- a/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.cpp +++ b/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.cpp @@ -1,266 +1,266 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "QmitkImageStatisticsCalculationThread.h" //QT headers #include #include #include #include #include QmitkImageStatisticsCalculationThread::QmitkImageStatisticsCalculationThread():QThread(), m_StatisticsImage(nullptr), m_BinaryMask(nullptr), m_PlanarFigureMask(nullptr), m_TimeStep(0), m_IgnoreZeros(false), m_CalculationSuccessful(false), m_StatisticChanged(false), m_HistogramBinSize(10.0), m_UseDefaultNBins(true), m_nBinsForHistogramStatistics(100), m_prioritizeNBinsOverBinSize(true) { } QmitkImageStatisticsCalculationThread::~QmitkImageStatisticsCalculationThread() { } void QmitkImageStatisticsCalculationThread::Initialize( mitk::Image::Pointer image, mitk::Image::Pointer binaryImage, mitk::PlanarFigure::Pointer planarFig ) { // reset old values if( this->m_StatisticsImage.IsNotNull() ) this->m_StatisticsImage = nullptr; if( this->m_BinaryMask.IsNotNull() ) this->m_BinaryMask = nullptr; if( this->m_PlanarFigureMask.IsNotNull()) this->m_PlanarFigureMask = nullptr; // set new values if passed in if(image.IsNotNull()) this->m_StatisticsImage = image->Clone(); if(binaryImage.IsNotNull()) this->m_BinaryMask = binaryImage->Clone(); if(planarFig.IsNotNull()) this->m_PlanarFigureMask = planarFig->Clone(); } void QmitkImageStatisticsCalculationThread::SetUseDefaultNBins(bool useDefault) { m_UseDefaultNBins = useDefault; } void QmitkImageStatisticsCalculationThread::SetTimeStep( int times ) { this->m_TimeStep = times; } int QmitkImageStatisticsCalculationThread::GetTimeStep() { return this->m_TimeStep; } -std::vector QmitkImageStatisticsCalculationThread::GetStatisticsData() +std::vector QmitkImageStatisticsCalculationThread::GetStatisticsData() { return this->m_StatisticsVector; } mitk::Image::Pointer QmitkImageStatisticsCalculationThread::GetStatisticsImage() { return this->m_StatisticsImage; } void QmitkImageStatisticsCalculationThread::SetIgnoreZeroValueVoxel(bool _arg) { this->m_IgnoreZeros = _arg; } bool QmitkImageStatisticsCalculationThread::GetIgnoreZeroValueVoxel() { return this->m_IgnoreZeros; } void QmitkImageStatisticsCalculationThread::SetHistogramBinSize(double size) { this->m_HistogramBinSize = size; this->m_prioritizeNBinsOverBinSize = false; } double QmitkImageStatisticsCalculationThread::GetHistogramBinSize() const { return this->m_HistogramBinSize; } void QmitkImageStatisticsCalculationThread::SetHistogramNBins(double size) { this->m_nBinsForHistogramStatistics = size; this->m_prioritizeNBinsOverBinSize = true; } double QmitkImageStatisticsCalculationThread::GetHistogramNBins() const { return this->m_nBinsForHistogramStatistics; } std::string QmitkImageStatisticsCalculationThread::GetLastErrorMessage() { return m_message; } QmitkImageStatisticsCalculationThread::HistogramType::Pointer QmitkImageStatisticsCalculationThread::GetTimeStepHistogram(unsigned int t) { if (t >= this->m_HistogramVector.size()) return nullptr; return this->m_HistogramVector[t]; } bool QmitkImageStatisticsCalculationThread::GetStatisticsChangedFlag() { return m_StatisticChanged; } bool QmitkImageStatisticsCalculationThread::GetStatisticsUpdateSuccessFlag() { return m_CalculationSuccessful; } void QmitkImageStatisticsCalculationThread::run() { bool statisticCalculationSuccessful = true; mitk::ImageStatisticsCalculator::Pointer calculator = mitk::ImageStatisticsCalculator::New(); if(this->m_StatisticsImage.IsNotNull()) { calculator->SetInputImage(m_StatisticsImage); } else { statisticCalculationSuccessful = false; } // Bug 13416 : The ImageStatistics::SetImageMask() method can throw exceptions, i.e. when the dimensionality // of the masked and input image differ, we need to catch them and mark the calculation as failed // the same holds for the ::SetPlanarFigure() try { if(this->m_BinaryMask.IsNotNull()) { mitk::ImageMaskGenerator::Pointer imgMask = mitk::ImageMaskGenerator::New(); imgMask->SetImageMask(m_BinaryMask); calculator->SetMask(imgMask.GetPointer()); } if(this->m_PlanarFigureMask.IsNotNull()) { mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_StatisticsImage); pfMaskGen->SetPlanarFigure(m_PlanarFigureMask); calculator->SetMask(pfMaskGen.GetPointer()); } } catch (const mitk::Exception& e) { MITK_ERROR << "MITK Exception: " << e.what(); statisticCalculationSuccessful = false; } catch (const itk::ExceptionObject& e) { MITK_ERROR << "ITK Exception:" << e.what(); statisticCalculationSuccessful = false; } catch ( const std::runtime_error &e ) { MITK_ERROR<< "Runtime Exception: " << e.what(); statisticCalculationSuccessful = false; } catch ( const std::exception &e ) { //m_message = "Failure: " + std::string(e.what()); MITK_ERROR<< "Standard Exception: " << e.what(); statisticCalculationSuccessful = false; } bool statisticChanged = false; if (this->m_IgnoreZeros) { mitk::IgnorePixelMaskGenerator::Pointer ignorePixelValueMaskGen = mitk::IgnorePixelMaskGenerator::New(); ignorePixelValueMaskGen->SetIgnoredPixelValue(0); ignorePixelValueMaskGen->SetInputImage(m_StatisticsImage); calculator->SetSecondaryMask(ignorePixelValueMaskGen.GetPointer()); } else { calculator->SetSecondaryMask(nullptr); } if (m_UseDefaultNBins) { calculator->SetNBinsForHistogramStatistics(100); } else { if (!m_prioritizeNBinsOverBinSize) { calculator->SetBinSizeForHistogramStatistics(m_HistogramBinSize); } else { calculator->SetNBinsForHistogramStatistics(100); } } //calculator->SetHistogramBinSize( m_HistogramBinSize ); //calculator->SetUseDefaultBinSize( m_UseDefaultBinSize ); for (unsigned int i = 0; i < m_StatisticsImage->GetTimeSteps(); i++) { try { calculator->GetStatistics(i); } catch ( mitk::Exception& e) { //m_message = e.GetDescription(); MITK_ERROR<< "MITK Exception: " << e.what(); statisticCalculationSuccessful = false; } catch ( const std::runtime_error &e ) { //m_message = "Failure: " + std::string(e.what()); MITK_ERROR<< "Runtime Exception: " << e.what(); statisticCalculationSuccessful = false; } catch ( const std::exception &e ) { //m_message = "Failure: " + std::string(e.what()); MITK_ERROR<< "Standard Exception: " << e.what(); statisticCalculationSuccessful = false; } } this->m_StatisticChanged = statisticChanged; this->m_CalculationSuccessful = statisticCalculationSuccessful; if(statisticCalculationSuccessful) { this->m_StatisticsVector.clear(); this->m_HistogramVector.clear(); for (unsigned int i = 0; i < m_StatisticsImage->GetTimeSteps(); i++) { this->m_StatisticsVector.push_back(calculator->GetStatistics(i)); this->m_HistogramVector.push_back((HistogramType*)this->m_StatisticsVector[i]->GetHistogram()); } } } diff --git a/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.h b/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.h index ab02f7d5af..3f962673ef 100644 --- a/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.h +++ b/Plugins/org.mitk.gui.qt.cest/src/QmitkImageStatisticsCalculationThread.h @@ -1,123 +1,123 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef QMITKIMAGESTATISTICSCALCULATIONTHREAD_H_INCLUDED #define QMITKIMAGESTATISTICSCALCULATIONTHREAD_H_INCLUDED //QT headers #include #include //mitk headers #include "mitkImage.h" #include "mitkPlanarFigure.h" #include "mitkImageStatisticsCalculator.h" // itk headers #ifndef __itkHistogram_h #include #endif /** /brief This class is executed as background thread for image statistics calculation. * Documentation: This class is derived from QThread and is intended to be used by QmitkImageStatisticsView to run the image statistics calculation in a background thread keepung the gui usable. * \ingroup Plugins/MeasurementToolbox */ class QmitkImageStatisticsCalculationThread : public QThread { Q_OBJECT public: typedef itk::Statistics::Histogram HistogramType; /*! /brief standard constructor. */ QmitkImageStatisticsCalculationThread(); /*! /brief standard destructor. */ ~QmitkImageStatisticsCalculationThread(); /*! *\brief Automatically calculate bin size to obtain 200 bins. */ void SetUseDefaultNBins(bool useDefault); /*! /brief Initializes the object with necessary data. */ void Initialize( mitk::Image::Pointer image, mitk::Image::Pointer binaryImage, mitk::PlanarFigure::Pointer planarFig ); /*! /brief returns the calculated image statistics. */ - std::vector GetStatisticsData(); + std::vector GetStatisticsData(); /*! /brief */ mitk::Image::Pointer GetStatisticsImage(); /*! /brief Set the time step of the image you want to process. */ void SetTimeStep( int times ); /*! /brief Get the time step of the image you want to process. */ int GetTimeStep(); /*! /brief Set flag to ignore zero valued voxels */ void SetIgnoreZeroValueVoxel( bool _arg ); /*! /brief Get status of zero value voxel ignoring. */ bool GetIgnoreZeroValueVoxel(); /*! /brief Set bin size for histogram resolution.*/ void SetHistogramBinSize( double size); /*! /brief Get bin size for histogram resolution.*/ double GetHistogramBinSize() const; /*! /brief Set bin size for histogram resolution.*/ void SetHistogramNBins( double size); /*! /brief Get bin size for histogram resolution.*/ double GetHistogramNBins() const; /*! /brief Returns the histogram of the currently selected time step. */ HistogramType::Pointer GetTimeStepHistogram(unsigned int t = 0); /*! /brief Returns a flag indicating if the statistics have changed during calculation */ bool GetStatisticsChangedFlag(); /*! /brief Returns a flag the indicates if the statistics are updated successfully */ bool GetStatisticsUpdateSuccessFlag(); /*! /brief Method called once the thread is executed. */ void run() override; std::string GetLastErrorMessage(); private: //member declaration mitk::Image::Pointer m_StatisticsImage; ///< member variable holds the input image for which the statistics need to be calculated. mitk::Image::Pointer m_BinaryMask; ///< member variable holds the binary mask image for segmentation image statistics calculation. mitk::PlanarFigure::Pointer m_PlanarFigureMask; ///< member variable holds the planar figure for segmentation image statistics calculation. - std::vector m_StatisticsVector; ///< member variable holds the result structs. + std::vector m_StatisticsVector; ///< member variable holds the result structs. int m_TimeStep; ///< member variable holds the time step for statistics calculation bool m_IgnoreZeros; ///< member variable holds flag to indicate if zero valued voxel should be suppressed bool m_CalculationSuccessful; ///< flag set if statistics calculation was successful bool m_StatisticChanged; ///< flag set if statistics have changed double m_HistogramBinSize; ///< member variable holds the bin size for histogram resolution. std::vector m_HistogramVector; ///< member holds the histograms of all time steps. std::string m_message; bool m_UseDefaultNBins; unsigned int m_nBinsForHistogramStatistics; bool m_prioritizeNBinsOverBinSize; }; #endif // QMITKIMAGESTATISTICSCALCULATIONTHREAD_H_INCLUDED diff --git a/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.cpp b/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.cpp index 3c4f8f51fb..3f7660c17c 100644 --- a/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.cpp +++ b/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.cpp @@ -1,899 +1,899 @@ ïğż/*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ //itk #include "itksys/SystemTools.hxx" #include #include // Blueberry #include #include // Qmitk #include "QmitkCESTStatisticsView.h" // Qt #include #include // qwt #include // mitk #include #include #include #include #include #include #include #include #include #include // boost #include #include //stl #include #include #include #include #include #include const std::string QmitkCESTStatisticsView::VIEW_ID = "org.mitk.views.ceststatistics"; static const int STAT_TABLE_BASE_HEIGHT = 180; QmitkCESTStatisticsView::QmitkCESTStatisticsView(QObject* /*parent*/, const char* /*name*/) { this->m_CalculatorThread = new QmitkImageStatisticsCalculationThread; m_currentSelectedPosition.Fill(0.0); m_currentSelectedTimeStep = 0; m_CrosshairPointSet = mitk::PointSet::New(); } QmitkCESTStatisticsView::~QmitkCESTStatisticsView() { while (this->m_CalculatorThread->isRunning()) // wait until thread has finished { itksys::SystemTools::Delay(100); } delete this->m_CalculatorThread; } void QmitkCESTStatisticsView::SetFocus() { m_Controls.threeDimToFourDimPushButton->setFocus(); } void QmitkCESTStatisticsView::CreateQtPartControl( QWidget *parent ) { // create GUI widgets from the Qt Designer's .ui file m_Controls.setupUi( parent ); connect(m_Controls.threeDimToFourDimPushButton, SIGNAL(clicked()), this, SLOT(OnThreeDimToFourDimPushButtonClicked())); connect((QObject*) this->m_CalculatorThread, SIGNAL(finished()), this, SLOT(OnThreadedStatisticsCalculationEnds()), Qt::QueuedConnection); connect((QObject*)(this->m_Controls.m_CopyStatisticsToClipboardPushButton), SIGNAL(clicked()), (QObject*) this, SLOT(OnCopyStatisticsToClipboardPushButtonClicked())); connect((QObject*)(this->m_Controls.normalizeImagePushButton), SIGNAL(clicked()), (QObject*) this, SLOT(OnNormalizeImagePushButtonClicked())); connect((QObject*)(this->m_Controls.fixedRangeCheckBox), SIGNAL(toggled(bool)), (QObject*) this, SLOT(OnFixedRangeCheckBoxToggled(bool))); connect((QObject*)(this->m_Controls.fixedRangeLowerDoubleSpinBox), SIGNAL(editingFinished()), (QObject*) this, SLOT(OnFixedRangeDoubleSpinBoxChanged())); connect((QObject*)(this->m_Controls.fixedRangeUpperDoubleSpinBox), SIGNAL(editingFinished()), (QObject*) this, SLOT(OnFixedRangeDoubleSpinBoxChanged())); m_Controls.normalizeImagePushButton->setEnabled(false); m_Controls.threeDimToFourDimPushButton->setEnabled(false); this->m_SliceChangeListener.RenderWindowPartActivated(this->GetRenderWindowPart()); connect(&m_SliceChangeListener, SIGNAL(SliceChanged()), this, SLOT(OnSliceChanged())); } void QmitkCESTStatisticsView::RenderWindowPartActivated(mitk::IRenderWindowPart* renderWindowPart) { this->m_SliceChangeListener.RenderWindowPartActivated(renderWindowPart); } void QmitkCESTStatisticsView::RenderWindowPartDeactivated( mitk::IRenderWindowPart* renderWindowPart) { this->m_SliceChangeListener.RenderWindowPartDeactivated(renderWindowPart); } void QmitkCESTStatisticsView::OnSelectionChanged( berry::IWorkbenchPart::Pointer /*source*/, const QList& nodes ) { if (nodes.empty()) { std::stringstream message; message << "Please select an image."; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); this->Clear(); return; } // iterate all selected objects bool atLeastOneWasCESTImage = false; foreach( mitk::DataNode::Pointer node, nodes ) { if (node.IsNull()) { continue; } if( dynamic_cast(node->GetData()) != nullptr ) { m_Controls.labelWarning->setVisible( false ); bool zSpectrumSet = SetZSpectrum(dynamic_cast(node->GetData()->GetProperty(mitk::CustomTagParser::m_OffsetsPropertyName.c_str()).GetPointer())); atLeastOneWasCESTImage = atLeastOneWasCESTImage || zSpectrumSet; if (zSpectrumSet) { m_ZImage = dynamic_cast(node->GetData()); } else { m_MaskImage = dynamic_cast(node->GetData()); } } if (dynamic_cast(node->GetData()) != nullptr) { m_MaskPlanarFigure = dynamic_cast(node->GetData()); } if (dynamic_cast(node->GetData()) != nullptr) { m_PointSet = dynamic_cast(node->GetData()); } } // We only want to offer normalization or timestep copying if one object is selected if (nodes.size() == 1) { if (dynamic_cast(nodes.front()->GetData()) ) { m_Controls.normalizeImagePushButton->setEnabled(atLeastOneWasCESTImage); m_Controls.threeDimToFourDimPushButton->setDisabled(atLeastOneWasCESTImage); } else { m_Controls.normalizeImagePushButton->setEnabled(false); m_Controls.threeDimToFourDimPushButton->setEnabled(false); std::stringstream message; message << "The selected node is not an image."; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); } this->Clear(); return; } // we always need a mask, either image or planar figure as well as an image for further processing if (nodes.size() != 2) { this->Clear(); return; } m_Controls.normalizeImagePushButton->setEnabled(false); m_Controls.threeDimToFourDimPushButton->setEnabled(false); if (!atLeastOneWasCESTImage) { std::stringstream message; message << "None of the selected data nodes contains required CEST meta information"; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); this->Clear(); return; } bool bothAreImages = (m_ZImage.GetPointer() != nullptr) && (m_MaskImage.GetPointer() != nullptr); if (bothAreImages) { bool geometriesMatch = mitk::Equal(*(m_ZImage->GetTimeGeometry()), *(m_MaskImage->GetTimeGeometry()), mitk::eps, false); if (!geometriesMatch) { std::stringstream message; message << "The selected images have different geometries."; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); this->Clear(); return; } } if (!this->DataSanityCheck()) { this->Clear(); return; } if (m_PointSet.IsNull()) { // initialize thread and trigger it this->m_CalculatorThread->SetIgnoreZeroValueVoxel(false); this->m_CalculatorThread->Initialize(m_ZImage, m_MaskImage, m_MaskPlanarFigure); std::stringstream message; message << "Calculating statistics..."; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); try { // Compute statistics this->m_CalculatorThread->start(); } catch (const mitk::Exception& e) { std::stringstream message; message << "" << e.GetDescription() << ""; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); } catch (const std::runtime_error &e) { // In case of exception, print error message on GUI std::stringstream message; message << "" << e.what() << ""; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); } catch (const std::exception &e) { MITK_ERROR << "Caught exception: " << e.what(); // In case of exception, print error message on GUI std::stringstream message; message << "Error! Unequal Dimensions of Image and Segmentation. No recompute possible "; m_Controls.labelWarning->setText(message.str().c_str()); m_Controls.labelWarning->show(); } while (this->m_CalculatorThread->isRunning()) // wait until thread has finished { itksys::SystemTools::Delay(100); } } if (m_PointSet.IsNotNull()) { if (m_ZImage->GetDimension() == 4) { AccessFixedDimensionByItk(m_ZImage, PlotPointSet, 4); } else { MITK_WARN << "Expecting a 4D image."; } } } void QmitkCESTStatisticsView::OnThreadedStatisticsCalculationEnds() { this->m_Controls.m_DataViewWidget->SetAxisTitle(QwtPlot::Axis::xBottom, "delta w"); this->m_Controls.m_DataViewWidget->SetAxisTitle(QwtPlot::Axis::yLeft, "z"); - const std::vector &statistics = + const std::vector &statistics = this->m_CalculatorThread->GetStatisticsData(); QmitkPlotWidget::DataVector::size_type numberOfSpectra = this->m_zSpectrum.size(); QmitkPlotWidget::DataVector means(numberOfSpectra); QmitkPlotWidget::DataVector stdevs(numberOfSpectra); for (unsigned int index = 0; index < numberOfSpectra; ++index) { means[index] = statistics[index]->GetMean(); stdevs[index] = statistics[index]->GetStd(); } QmitkPlotWidget::DataVector xValues = this->m_zSpectrum; RemoveMZeros(xValues, means, stdevs); unsigned int curveId = this->m_Controls.m_DataViewWidget->InsertCurve("Spectrum"); this->m_Controls.m_DataViewWidget->SetCurveData(curveId, xValues, means, stdevs, stdevs); this->m_Controls.m_DataViewWidget->SetErrorPen(curveId, QPen(Qt::blue)); QwtSymbol* blueSymbol = new QwtSymbol(QwtSymbol::Rect, QColor(Qt::blue), QColor(Qt::blue), QSize(8, 8)); this->m_Controls.m_DataViewWidget->SetCurveSymbol(curveId, blueSymbol); this->m_Controls.m_DataViewWidget->SetLegendAttribute(curveId, QwtPlotCurve::LegendShowSymbol); QwtLegend* legend = new QwtLegend(); legend->setFrameShape(QFrame::Box); legend->setFrameShadow(QFrame::Sunken); legend->setLineWidth(1); this->m_Controls.m_DataViewWidget->SetLegend(legend, QwtPlot::BottomLegend); m_Controls.m_DataViewWidget->GetPlot()->axisScaleEngine(QwtPlot::Axis::xBottom)->setAttributes(QwtScaleEngine::Inverted); this->m_Controls.m_DataViewWidget->Replot(); m_Controls.labelWarning->setVisible(false); if (this->m_Controls.fixedRangeCheckBox->isChecked()) { this->m_Controls.m_DataViewWidget->GetPlot()->setAxisAutoScale(2, false); this->m_Controls.m_DataViewWidget->GetPlot()->setAxisScale(2, this->m_Controls.fixedRangeLowerDoubleSpinBox->value(), this->m_Controls.fixedRangeUpperDoubleSpinBox->value()); } else { this->m_Controls.m_DataViewWidget->GetPlot()->setAxisAutoScale(2, true); } if(this->DataSanityCheck()) { this->FillStatisticsTableView(this->m_CalculatorThread->GetStatisticsData(), this->m_CalculatorThread->GetStatisticsImage()); } else { this->Clear(); } } void QmitkCESTStatisticsView::OnFixedRangeDoubleSpinBoxChanged() { if (this->m_Controls.fixedRangeCheckBox->isChecked()) { this->m_Controls.m_DataViewWidget->GetPlot()->setAxisAutoScale(2, false); this->m_Controls.m_DataViewWidget->GetPlot()->setAxisScale(2, this->m_Controls.fixedRangeLowerDoubleSpinBox->value(), this->m_Controls.fixedRangeUpperDoubleSpinBox->value()); } this->m_Controls.m_DataViewWidget->Replot(); } template void QmitkCESTStatisticsView::PlotPointSet(itk::Image* image) { this->m_Controls.m_DataViewWidget->SetAxisTitle(QwtPlot::Axis::xBottom, "delta w"); this->m_Controls.m_DataViewWidget->SetAxisTitle(QwtPlot::Axis::yLeft, "z"); QmitkPlotWidget::DataVector::size_type numberOfSpectra = this->m_zSpectrum.size(); mitk::PointSet::Pointer internalPointset; if (m_PointSet.IsNotNull()) { internalPointset = m_PointSet; } else { internalPointset = m_CrosshairPointSet; } if (internalPointset.IsNull()) { return; } auto maxIndex = internalPointset->GetMaxId().Index(); for (std::size_t number = 0; number < maxIndex + 1; ++number) { mitk::PointSet::PointType point; if (!internalPointset->GetPointIfExists(number, &point)) { continue; } if (!this->m_ZImage->GetGeometry()->IsInside(point)) { continue; } itk::Index<3> itkIndex; this->m_ZImage->GetGeometry()->WorldToIndex(point, itkIndex); itk::Index itkIndexTime; itkIndexTime[0] = itkIndex[0]; itkIndexTime[1] = itkIndex[1]; itkIndexTime[2] = itkIndex[2]; QmitkPlotWidget::DataVector values(numberOfSpectra); for (std::size_t step = 0; step < numberOfSpectra; ++step) { if( VImageDimension == 4 ) { itkIndexTime[3] = step; } values[step] = image->GetPixel(itkIndexTime); } std::stringstream name; name << "Point " << number; // Qcolor enums go from 0 to 19, but 19 is transparent and 0,1 are for bitmaps // 3 is white and thus not visible QColor color(static_cast(number % 17 + 4)); QmitkPlotWidget::DataVector xValues = this->m_zSpectrum; RemoveMZeros(xValues, values); unsigned int curveId = this->m_Controls.m_DataViewWidget->InsertCurve(name.str().c_str()); this->m_Controls.m_DataViewWidget->SetCurveData(curveId, xValues, values); this->m_Controls.m_DataViewWidget->SetCurvePen(curveId, QPen(color)); QwtSymbol* symbol = new QwtSymbol(QwtSymbol::Rect, color, color, QSize(8, 8)); this->m_Controls.m_DataViewWidget->SetCurveSymbol(curveId, symbol); this->m_Controls.m_DataViewWidget->SetLegendAttribute(curveId, QwtPlotCurve::LegendShowSymbol); } if (this->m_Controls.fixedRangeCheckBox->isChecked()) { this->m_Controls.m_DataViewWidget->GetPlot()->setAxisAutoScale(2, false); this->m_Controls.m_DataViewWidget->GetPlot()->setAxisScale(2, this->m_Controls.fixedRangeLowerDoubleSpinBox->value(), this->m_Controls.fixedRangeUpperDoubleSpinBox->value()); } else { this->m_Controls.m_DataViewWidget->GetPlot()->setAxisAutoScale(2, true); } QwtLegend* legend = new QwtLegend(); legend->setFrameShape(QFrame::Box); legend->setFrameShadow(QFrame::Sunken); legend->setLineWidth(1); this->m_Controls.m_DataViewWidget->SetLegend(legend, QwtPlot::BottomLegend); m_Controls.m_DataViewWidget->GetPlot()->axisScaleEngine(QwtPlot::Axis::xBottom)->setAttributes(QwtScaleEngine::Inverted); this->m_Controls.m_DataViewWidget->Replot(); m_Controls.labelWarning->setVisible(false); } void QmitkCESTStatisticsView::OnFixedRangeCheckBoxToggled(bool state) { this->m_Controls.fixedRangeLowerDoubleSpinBox->setEnabled(state); this->m_Controls.fixedRangeUpperDoubleSpinBox->setEnabled(state); } void QmitkCESTStatisticsView::OnNormalizeImagePushButtonClicked() { QList nodes = this->GetDataManagerSelection(); if (nodes.empty()) return; mitk::DataNode* node = nodes.front(); if (!node) { // Nothing selected. Inform the user and return QMessageBox::information(nullptr, "CEST View", "Please load and select an image before starting image processing."); return; } // here we have a valid mitk::DataNode // a node itself is not very useful, we need its data item (the image) mitk::BaseData* data = node->GetData(); if (data) { // test if this data item is an image or not (could also be a surface or something totally different) mitk::Image* image = dynamic_cast(data); if (image) { std::string offsets = ""; bool hasOffsets = image->GetPropertyList()->GetStringProperty( mitk::CustomTagParser::m_OffsetsPropertyName.c_str() ,offsets); if (!hasOffsets) { QMessageBox::information(nullptr, "CEST View", "Selected image was missing CEST offset information."); return; } if (image->GetDimension() == 4) { auto normalizationFilter = mitk::CESTImageNormalizationFilter::New(); normalizationFilter->SetInput(image); normalizationFilter->Update(); auto resultImage = normalizationFilter->GetOutput(); mitk::DataNode::Pointer dataNode = mitk::DataNode::New(); dataNode->SetData(resultImage); std::string normalizedName = node->GetName() + "_normalized"; dataNode->SetName(normalizedName); this->GetDataStorage()->Add(dataNode); } this->Clear(); } } } void QmitkCESTStatisticsView::RemoveMZeros(QmitkPlotWidget::DataVector& xValues, QmitkPlotWidget::DataVector& yValues) { QmitkPlotWidget::DataVector tempX; QmitkPlotWidget::DataVector tempY; for (std::size_t index = 0; index < xValues.size(); ++index) { if ((xValues.at(index) < -299) || (xValues.at(index)) > 299) { // do not include } else { tempX.push_back(xValues.at(index)); tempY.push_back(yValues.at(index)); } } xValues = tempX; yValues = tempY; } void QmitkCESTStatisticsView::RemoveMZeros(QmitkPlotWidget::DataVector& xValues, QmitkPlotWidget::DataVector& yValues, QmitkPlotWidget::DataVector& stdDevs) { QmitkPlotWidget::DataVector tempX; QmitkPlotWidget::DataVector tempY; QmitkPlotWidget::DataVector tempDevs; for (std::size_t index = 0; index < xValues.size(); ++index) { if ((xValues.at(index) < -299) || (xValues.at(index)) > 299) { // do not include } else { tempX.push_back(xValues.at(index)); tempY.push_back(yValues.at(index)); tempDevs.push_back(stdDevs.at(index)); } } xValues = tempX; yValues = tempY; stdDevs = tempDevs; } void QmitkCESTStatisticsView::OnThreeDimToFourDimPushButtonClicked() { QList nodes = this->GetDataManagerSelection(); if (nodes.empty()) return; mitk::DataNode* node = nodes.front(); if (!node) { // Nothing selected. Inform the user and return QMessageBox::information( nullptr, "CEST View", "Please load and select an image before starting image processing."); return; } // here we have a valid mitk::DataNode // a node itself is not very useful, we need its data item (the image) mitk::BaseData* data = node->GetData(); if (data) { // test if this data item is an image or not (could also be a surface or something totally different) mitk::Image* image = dynamic_cast( data ); if (image) { if (image->GetDimension() == 4) { AccessFixedDimensionByItk(image, CopyTimesteps, 4); } this->Clear(); } } } template void QmitkCESTStatisticsView::CopyTimesteps(itk::Image* image) { typedef itk::Image ImageType; //typedef itk::PasteImageFilter PasteImageFilterType; unsigned int numberOfTimesteps = image->GetLargestPossibleRegion().GetSize(3); typename ImageType::RegionType sourceRegion = image->GetLargestPossibleRegion(); sourceRegion.SetSize(3, 1); typename ImageType::RegionType targetRegion = image->GetLargestPossibleRegion(); targetRegion.SetSize(3, 1); for (unsigned int timestep = 1; timestep < numberOfTimesteps; ++timestep) { targetRegion.SetIndex(3, timestep); itk::ImageRegionConstIterator sourceIterator(image, sourceRegion); itk::ImageRegionIterator targetIterator(image, targetRegion); while (!sourceIterator.IsAtEnd()) { targetIterator.Set(sourceIterator.Get()); ++sourceIterator; ++targetIterator; } } } bool QmitkCESTStatisticsView::SetZSpectrum(mitk::StringProperty* zSpectrumProperty) { if (zSpectrumProperty == nullptr) { return false; } mitk::LocaleSwitch localeSwitch("C"); std::string zSpectrumString = zSpectrumProperty->GetValueAsString(); std::istringstream iss(zSpectrumString); std::vector zSpectra; std::copy(std::istream_iterator(iss), std::istream_iterator(), std::back_inserter(zSpectra)); m_zSpectrum.clear(); m_zSpectrum.resize(0); for (auto const &spectrumString : zSpectra) { m_zSpectrum.push_back(std::stod(spectrumString)); } return (m_zSpectrum.size() > 0); } void QmitkCESTStatisticsView::FillStatisticsTableView( - const std::vector &s, + const std::vector &s, const mitk::Image *image) { this->m_Controls.m_StatisticsTable->setColumnCount(image->GetTimeSteps()); this->m_Controls.m_StatisticsTable->horizontalHeader()->setVisible(image->GetTimeSteps() > 1); int decimals = 2; mitk::PixelType doublePix = mitk::MakeScalarPixelType< double >(); mitk::PixelType floatPix = mitk::MakeScalarPixelType< float >(); if (image->GetPixelType() == doublePix || image->GetPixelType() == floatPix) { decimals = 5; } for (unsigned int t = 0; t < image->GetTimeSteps(); t++) { this->m_Controls.m_StatisticsTable->setHorizontalHeaderItem(t, new QTableWidgetItem(QString::number(m_zSpectrum[t]))); this->m_Controls.m_StatisticsTable->setItem(0, t, new QTableWidgetItem( QString("%1").arg(s[t]->GetMean(), 0, 'f', decimals))); this->m_Controls.m_StatisticsTable->setItem(1, t, new QTableWidgetItem( QString("%1").arg(s[t]->GetStd(), 0, 'f', decimals))); this->m_Controls.m_StatisticsTable->setItem(2, t, new QTableWidgetItem( QString("%1").arg(s[t]->GetRMS(), 0, 'f', decimals))); QString max; max.append(QString("%1").arg(s[t]->GetMax(), 0, 'f', decimals)); max += " ("; for (unsigned int i = 0; iGetMaxIndex().size(); i++) { max += QString::number(s[t]->GetMaxIndex()[i]); if (iGetMaxIndex().size() - 1) max += ","; } max += ")"; this->m_Controls.m_StatisticsTable->setItem(3, t, new QTableWidgetItem(max)); QString min; min.append(QString("%1").arg(s[t]->GetMin(), 0, 'f', decimals)); min += " ("; for (unsigned int i = 0; iGetMinIndex().size(); i++) { min += QString::number(s[t]->GetMinIndex()[i]); if (iGetMinIndex().size() - 1) min += ","; } min += ")"; this->m_Controls.m_StatisticsTable->setItem(4, t, new QTableWidgetItem(min)); this->m_Controls.m_StatisticsTable->setItem(5, t, new QTableWidgetItem( QString("%1").arg(s[t]->GetN()))); const mitk::BaseGeometry *geometry = image->GetGeometry(); if (geometry != nullptr) { const mitk::Vector3D &spacing = image->GetGeometry()->GetSpacing(); double volume = spacing[0] * spacing[1] * spacing[2] * (double)s[t]->GetN(); this->m_Controls.m_StatisticsTable->setItem(6, t, new QTableWidgetItem( QString("%1").arg(volume, 0, 'f', decimals))); } else { this->m_Controls.m_StatisticsTable->setItem(6, t, new QTableWidgetItem( "NA")); } } this->m_Controls.m_StatisticsTable->resizeColumnsToContents(); int height = STAT_TABLE_BASE_HEIGHT; if (this->m_Controls.m_StatisticsTable->horizontalHeader()->isVisible()) height += this->m_Controls.m_StatisticsTable->horizontalHeader()->height(); //if (this->m_Controls.m_StatisticsTable->horizontalScrollBar()->isVisible()) // height += this->m_Controls.m_StatisticsTable->horizontalScrollBar()->height(); this->m_Controls.m_StatisticsTable->setMinimumHeight(height); this->m_Controls.m_StatisticsGroupBox->setEnabled(true); this->m_Controls.m_StatisticsTable->setEnabled(true); } void QmitkCESTStatisticsView::InvalidateStatisticsTableView() { this->m_Controls.m_StatisticsTable->horizontalHeader()->setVisible(false); this->m_Controls.m_StatisticsTable->setColumnCount(1); for (int i = 0; i < this->m_Controls.m_StatisticsTable->rowCount(); ++i) { { this->m_Controls.m_StatisticsTable->setItem(i, 0, new QTableWidgetItem("NA")); } } this->m_Controls.m_StatisticsTable->setMinimumHeight(STAT_TABLE_BASE_HEIGHT); this->m_Controls.m_StatisticsTable->setEnabled(false); } bool QmitkCESTStatisticsView::DataSanityCheck() { QmitkPlotWidget::DataVector::size_type numberOfSpectra = m_zSpectrum.size(); // if we do not have a spectrum, the data can not be processed if (numberOfSpectra == 0) { return false; } // if we do not have CEST image data, the data can not be processed if (m_ZImage.IsNull()) { return false; } // if the CEST image data and the meta information do not match, the data can not be processed if (numberOfSpectra != m_ZImage->GetTimeSteps()) { return false; } // if we have neither a mask image, a point set nor a mask planar figure, we can not do statistics // statistics on the whole image would not make sense if (m_MaskImage.IsNull() && m_MaskPlanarFigure.IsNull() && m_PointSet.IsNull() ) { return false; } // if we have a mask image and a mask planar figure, we can not do statistics // we do not know which one to use if (m_MaskImage.IsNotNull() && m_MaskPlanarFigure.IsNotNull()) { return false; } return true; } void QmitkCESTStatisticsView::Clear() { this->m_zSpectrum.clear(); this->m_zSpectrum.resize(0); this->m_ZImage = nullptr; this->m_MaskImage = nullptr; this->m_MaskPlanarFigure = nullptr; this->m_PointSet = nullptr; this->m_Controls.m_DataViewWidget->Clear(); this->InvalidateStatisticsTableView(); this->m_Controls.m_StatisticsGroupBox->setEnabled(false); } void QmitkCESTStatisticsView::OnCopyStatisticsToClipboardPushButtonClicked() { QLocale tempLocal; QLocale::setDefault(QLocale(QLocale::English, QLocale::UnitedStates)); - const std::vector &statistics = + const std::vector &statistics = this->m_CalculatorThread->GetStatisticsData(); QmitkPlotWidget::DataVector::size_type size = m_zSpectrum.size(); QString clipboard("delta_w \t Mean \t StdDev \t RMS \t Max \t Min \t N\n"); for (QmitkPlotWidget::DataVector::size_type index = 0; index < size; ++index) { // Copy statistics to clipboard ("%Ln" will use the default locale for // number formatting) clipboard = clipboard.append("%L1 \t %L2 \t %L3 \t %L4 \t %L5 \t %L6 \t %L7\n") .arg(m_zSpectrum[index], 0, 'f', 10) .arg(statistics[index]->GetMean(), 0, 'f', 10) .arg(statistics[index]->GetStd(), 0, 'f', 10) .arg(statistics[index]->GetRMS(), 0, 'f', 10) .arg(statistics[index]->GetMax(), 0, 'f', 10) .arg(statistics[index]->GetMin(), 0, 'f', 10) .arg(statistics[index]->GetN()); } QApplication::clipboard()->setText( clipboard, QClipboard::Clipboard); QLocale::setDefault(tempLocal); } void QmitkCESTStatisticsView::OnSliceChanged() { mitk::Point3D currentSelectedPosition = this->GetRenderWindowPart()->GetSelectedPosition(nullptr); unsigned int currentSelectedTimeStep = this->GetRenderWindowPart()->GetTimeNavigationController()->GetTime()->GetPos(); if (m_currentSelectedPosition != currentSelectedPosition || m_currentSelectedTimeStep != currentSelectedTimeStep) //|| m_selectedNodeTime > m_currentPositionTime) { //the current position has been changed or the selected node has been changed since the last position validation -> check position m_currentSelectedPosition = currentSelectedPosition; m_currentSelectedTimeStep = currentSelectedTimeStep; m_currentPositionTime.Modified(); m_CrosshairPointSet->Clear(); m_CrosshairPointSet->SetPoint(0, m_currentSelectedPosition); QList nodes = this->GetDataManagerSelection(); if (nodes.empty() || nodes.size() > 1) return; mitk::DataNode* node = nodes.front(); if (!node) { return; } if (dynamic_cast(node->GetData()) != nullptr) { m_Controls.labelWarning->setVisible(false); bool zSpectrumSet = SetZSpectrum(dynamic_cast( node->GetData()->GetProperty(mitk::CustomTagParser::m_OffsetsPropertyName.c_str()).GetPointer())); if (zSpectrumSet) { m_ZImage = dynamic_cast(node->GetData()); } else { return; } } else { return; } this->m_Controls.m_DataViewWidget->Clear(); AccessFixedDimensionByItk(m_ZImage, PlotPointSet, 4); } } diff --git a/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.h b/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.h index 6a7cd33fc4..948b274c54 100644 --- a/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.h +++ b/Plugins/org.mitk.gui.qt.cest/src/internal/QmitkCESTStatisticsView.h @@ -1,153 +1,153 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef QmitkCESTStatisticsView_h #define QmitkCESTStatisticsView_h #include #include #include #include "ui_QmitkCESTStatisticsViewControls.h" #include #include #include /** \brief QmitkCESTStatisticsView \warning Basic statistics view for CEST data. \sa QmitkAbstractView \ingroup ${plugin_target}_internal */ class QmitkCESTStatisticsView : public QmitkAbstractView, public mitk::IRenderWindowPartListener { // this is needed for all Qt objects that should have a Qt meta-object // (everything that derives from QObject and wants to have signal/slots) Q_OBJECT public: static const std::string VIEW_ID; /*! \brief default constructor */ QmitkCESTStatisticsView(QObject *parent = nullptr, const char *name = nullptr); /*! \brief default destructor */ virtual ~QmitkCESTStatisticsView(); protected slots: /// \brief Called when the user clicks the GUI button void OnThreeDimToFourDimPushButtonClicked(); /// \brief takes care of processing the computed data void OnThreadedStatisticsCalculationEnds(); /// \brief copy statistics to clipboard void OnCopyStatisticsToClipboardPushButtonClicked(); /// \brief normalize cest image void OnNormalizeImagePushButtonClicked(); /// \brief Toggle whether or not the plot uses a fixed x range void OnFixedRangeCheckBoxToggled(bool state); /// \brief Adapt axis scale when manual ranges are set void OnFixedRangeDoubleSpinBoxChanged(); /// \brief What to do if the crosshair moves void OnSliceChanged(); protected: virtual void CreateQtPartControl(QWidget *parent) override; virtual void SetFocus() override; virtual void RenderWindowPartActivated(mitk::IRenderWindowPart* renderWindowPart); virtual void RenderWindowPartDeactivated(mitk::IRenderWindowPart* renderWindowPart); /// \brief called by QmitkFunctionality when DataManager's selection has changed virtual void OnSelectionChanged( berry::IWorkbenchPart::Pointer source, const QList& nodes ) override; /** \brief Writes the calculated statistics to the GUI */ - void FillStatisticsTableView(const std::vector &s, + void FillStatisticsTableView(const std::vector &s, const mitk::Image *image); /** \brief Removes statistics from the GUI */ void InvalidateStatisticsTableView(); /// parse string and set data vector returns true if succesfull bool SetZSpectrum(mitk::StringProperty* zSpectrumProperty); /** Checks whether the currently set data appears reasonable */ bool DataSanityCheck(); /** Fills the plot based on a point set * * This will only use the first timestep */ template void PlotPointSet(itk::Image* image); /** Deletes all data */ void Clear(); /** Remove MZeros * * Will remove the data for the M0 images from the given input */ void RemoveMZeros(QmitkPlotWidget::DataVector& xValues, QmitkPlotWidget::DataVector& yValues); void RemoveMZeros(QmitkPlotWidget::DataVector& xValues, QmitkPlotWidget::DataVector& yValues, QmitkPlotWidget::DataVector& stdDevs); /** Copies the first timestep of a segmentation to all others */ template void CopyTimesteps(itk::Image* image); Ui::QmitkCESTStatisticsViewControls m_Controls; QmitkImageStatisticsCalculationThread* m_CalculatorThread; QmitkPlotWidget::DataVector m_zSpectrum; mitk::Image::Pointer m_ZImage; mitk::Image::Pointer m_MaskImage; mitk::PlanarFigure::Pointer m_MaskPlanarFigure; mitk::PointSet::Pointer m_PointSet; mitk::PointSet::Pointer m_CrosshairPointSet; QmitkSliceNavigationListener m_SliceChangeListener; itk::TimeStamp m_selectedNodeTime; itk::TimeStamp m_currentPositionTime; /** @brief currently valid selected position in the inspector*/ mitk::Point3D m_currentSelectedPosition; /** @brief indicates if the currently selected position is valid for the currently selected fit. * This it is within the input image */ unsigned int m_currentSelectedTimeStep; }; #endif // QmitkCESTStatisticsView_h diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.cpp b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.cpp index 5e2a6fa437..7a85121a59 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.cpp +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.cpp @@ -1,238 +1,238 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "QmitkImageStatisticsCalculationThread.h" //QT headers #include #include #include #include #include QmitkImageStatisticsCalculationThread::QmitkImageStatisticsCalculationThread() : QThread() , m_StatisticsImage(nullptr) , m_BinaryMask(nullptr) , m_PlanarFigureMask(nullptr) , m_TimeStep(0) , m_IgnoreZeros(false) , m_HistogramNBins(100) , m_StatisticChanged(false) , m_CalculationSuccessful(false) { } QmitkImageStatisticsCalculationThread::~QmitkImageStatisticsCalculationThread() { } void QmitkImageStatisticsCalculationThread::Initialize( mitk::Image::Pointer image, mitk::Image::Pointer binaryImage, mitk::PlanarFigure::Pointer planarFig ) { // reset old values if( this->m_StatisticsImage.IsNotNull() ) this->m_StatisticsImage = nullptr; if( this->m_BinaryMask.IsNotNull() ) this->m_BinaryMask = nullptr; if( this->m_PlanarFigureMask.IsNotNull()) this->m_PlanarFigureMask = nullptr; // set new values if passed in if(image.IsNotNull()) this->m_StatisticsImage = image->Clone(); if(binaryImage.IsNotNull()) this->m_BinaryMask = binaryImage->Clone(); if(planarFig.IsNotNull()) this->m_PlanarFigureMask = planarFig->Clone(); } void QmitkImageStatisticsCalculationThread::SetTimeStep( int times ) { this->m_TimeStep = times; } int QmitkImageStatisticsCalculationThread::GetTimeStep() { return this->m_TimeStep; } -std::vector QmitkImageStatisticsCalculationThread::GetStatisticsData() +std::vector QmitkImageStatisticsCalculationThread::GetStatisticsData() { return this->m_StatisticsVector; } mitk::Image::Pointer QmitkImageStatisticsCalculationThread::GetStatisticsImage() { return this->m_StatisticsImage; } void QmitkImageStatisticsCalculationThread::SetIgnoreZeroValueVoxel(bool _arg) { this->m_IgnoreZeros = _arg; } bool QmitkImageStatisticsCalculationThread::GetIgnoreZeroValueVoxel() { return this->m_IgnoreZeros; } void QmitkImageStatisticsCalculationThread::SetHistogramNBins(unsigned int nbins) { this->m_HistogramNBins = nbins; } unsigned int QmitkImageStatisticsCalculationThread::GetHistogramNBins() const { return this->m_HistogramNBins; } std::string QmitkImageStatisticsCalculationThread::GetLastErrorMessage() { return m_message; } QmitkImageStatisticsCalculationThread::HistogramType::Pointer QmitkImageStatisticsCalculationThread::GetTimeStepHistogram(unsigned int t) { if (t >= this->m_HistogramVector.size()) return nullptr; return this->m_HistogramVector[t]; } bool QmitkImageStatisticsCalculationThread::GetStatisticsChangedFlag() { return m_StatisticChanged; } bool QmitkImageStatisticsCalculationThread::GetStatisticsUpdateSuccessFlag() { return m_CalculationSuccessful; } void QmitkImageStatisticsCalculationThread::run() { bool statisticCalculationSuccessful = true; mitk::ImageStatisticsCalculator::Pointer calculator = mitk::ImageStatisticsCalculator::New(); if(this->m_StatisticsImage.IsNotNull()) { calculator->SetInputImage(m_StatisticsImage); } else { statisticCalculationSuccessful = false; } // Bug 13416 : The ImageStatistics::SetImageMask() method can throw exceptions, i.e. when the dimensionality // of the masked and input image differ, we need to catch them and mark the calculation as failed // the same holds for the ::SetPlanarFigure() try { if(this->m_BinaryMask.IsNotNull()) { mitk::ImageMaskGenerator::Pointer imgMask = mitk::ImageMaskGenerator::New(); imgMask->SetImageMask(m_BinaryMask); calculator->SetMask(imgMask.GetPointer()); } if(this->m_PlanarFigureMask.IsNotNull()) { mitk::PlanarFigureMaskGenerator::Pointer pfMaskGen = mitk::PlanarFigureMaskGenerator::New(); pfMaskGen->SetInputImage(m_StatisticsImage); pfMaskGen->SetPlanarFigure(m_PlanarFigureMask); calculator->SetMask(pfMaskGen.GetPointer()); } } catch (const mitk::Exception& e) { MITK_ERROR << "MITK Exception: " << e.what(); statisticCalculationSuccessful = false; } catch( const itk::ExceptionObject& e) { MITK_ERROR << "ITK Exception:" << e.what(); statisticCalculationSuccessful = false; } catch ( const std::runtime_error &e ) { MITK_ERROR<< "Runtime Exception: " << e.what(); statisticCalculationSuccessful = false; } catch ( const std::exception &e ) { MITK_ERROR<< "Standard Exception: " << e.what(); statisticCalculationSuccessful = false; } bool statisticChanged = false; if (this->m_IgnoreZeros) { mitk::IgnorePixelMaskGenerator::Pointer ignorePixelValueMaskGen = mitk::IgnorePixelMaskGenerator::New(); ignorePixelValueMaskGen->SetIgnoredPixelValue(0); ignorePixelValueMaskGen->SetInputImage(m_StatisticsImage); calculator->SetSecondaryMask(ignorePixelValueMaskGen.GetPointer()); } else { calculator->SetSecondaryMask(nullptr); } calculator->SetNBinsForHistogramStatistics(m_HistogramNBins); for (unsigned int i = 0; i < m_StatisticsImage->GetTimeSteps(); i++) { try { calculator->GetStatistics(i); } catch ( mitk::Exception& e) { //m_message = e.GetDescription(); MITK_ERROR<< "MITK Exception: " << e.what(); statisticCalculationSuccessful = false; } catch ( const std::runtime_error &e ) { //m_message = "Failure: " + std::string(e.what()); MITK_ERROR<< "Runtime Exception: " << e.what(); statisticCalculationSuccessful = false; } catch ( const std::exception &e ) { //m_message = "Failure: " + std::string(e.what()); MITK_ERROR<< "Standard Exception: " << e.what(); statisticCalculationSuccessful = false; } } this->m_StatisticChanged = statisticChanged; this->m_CalculationSuccessful = statisticCalculationSuccessful; if(statisticCalculationSuccessful) { this->m_StatisticsVector.clear(); this->m_HistogramVector.clear(); for (unsigned int i = 0; i < m_StatisticsImage->GetTimeSteps(); i++) { this->m_StatisticsVector.push_back(calculator->GetStatistics(i)); this->m_HistogramVector.push_back((HistogramType*)this->m_StatisticsVector[i]->GetHistogram()); } } } diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.h b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.h index 0f547ce224..970f29e696 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.h +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsCalculationThread.h @@ -1,112 +1,112 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef QMITKIMAGESTATISTICSCALCULATIONTHREAD_H_INCLUDED #define QMITKIMAGESTATISTICSCALCULATIONTHREAD_H_INCLUDED //QT headers #include #include //mitk headers #include "mitkImage.h" #include "mitkPlanarFigure.h" #include "mitkImageStatisticsCalculator.h" // itk headers #ifndef __itkHistogram_h #include #endif /** /brief This class is executed as background thread for image statistics calculation. * Documentation: This class is derived from QThread and is intended to be used by QmitkImageStatisticsView to run the image statistics calculation in a background thread keepung the gui usable. * \ingroup Plugins/MeasurementToolbox */ class QmitkImageStatisticsCalculationThread : public QThread { Q_OBJECT public: typedef itk::Statistics::Histogram HistogramType; /*! /brief standard constructor. */ QmitkImageStatisticsCalculationThread(); /*! /brief standard destructor. */ ~QmitkImageStatisticsCalculationThread(); /*! /brief Initializes the object with necessary data. */ void Initialize( mitk::Image::Pointer image, mitk::Image::Pointer binaryImage, mitk::PlanarFigure::Pointer planarFig ); /*! /brief returns the calculated image statistics. */ - std::vector GetStatisticsData(); + std::vector GetStatisticsData(); /*! /brief */ mitk::Image::Pointer GetStatisticsImage(); /*! /brief Set the time step of the image you want to process. */ void SetTimeStep( int times ); /*! /brief Get the time step of the image you want to process. */ int GetTimeStep(); /*! /brief Set flag to ignore zero valued voxels */ void SetIgnoreZeroValueVoxel( bool _arg ); /*! /brief Get status of zero value voxel ignoring. */ bool GetIgnoreZeroValueVoxel(); /*! /brief Set bin size for histogram resolution.*/ void SetHistogramNBins( unsigned int nbins); /*! /brief Get bin size for histogram resolution.*/ unsigned int GetHistogramNBins() const; /*! /brief Returns the histogram of the currently selected time step. */ HistogramType::Pointer GetTimeStepHistogram(unsigned int t = 0); /*! /brief Returns a flag indicating if the statistics have changed during calculation */ bool GetStatisticsChangedFlag(); /*! /brief Returns a flag the indicates if the statistics are updated successfully */ bool GetStatisticsUpdateSuccessFlag(); /*! /brief Method called once the thread is executed. */ void run() override; std::string GetLastErrorMessage(); private: //member declaration mitk::Image::Pointer m_StatisticsImage; ///< member variable holds the input image for which the statistics need to be calculated. mitk::Image::Pointer m_BinaryMask; ///< member variable holds the binary mask image for segmentation image statistics calculation. mitk::PlanarFigure::Pointer m_PlanarFigureMask; ///< member variable holds the planar figure for segmentation image statistics calculation. - std::vector m_StatisticsVector; ///< member variable holds the result structs. + std::vector m_StatisticsVector; ///< member variable holds the result structs. int m_TimeStep; ///< member variable holds the time step for statistics calculation bool m_IgnoreZeros; ///< member variable holds flag to indicate if zero valued voxel should be suppressed unsigned int m_HistogramNBins; ///< member variable holds the bin size for histogram resolution. bool m_StatisticChanged; ///< flag set if statistics have changed bool m_CalculationSuccessful; ///< flag set if statistics calculation was successful std::vector m_HistogramVector; ///< member holds the histograms of all time steps. std::string m_message; }; #endif // QMITKIMAGESTATISTICSCALCULATIONTHREAD_H_INCLUDED diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.cpp b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.cpp index e72394e3f8..0e19389768 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.cpp +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.cpp @@ -1,1305 +1,1313 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "QmitkImageStatisticsView.h" // Qt includes #include #include #include // berry includes #include // mitk includes #include #include #include #include #include #include #include // itk includes #include "itksys/SystemTools.hxx" #include "itkImageRegionConstIteratorWithIndex.h" #include //blueberry includes #include #include const std::string QmitkImageStatisticsView::VIEW_ID = "org.mitk.views.imagestatistics"; const int QmitkImageStatisticsView::STAT_TABLE_BASE_HEIGHT = 180; QmitkImageStatisticsView::QmitkImageStatisticsView(QObject* /*parent*/, const char* /*name*/) : m_Controls( nullptr ), m_SelectedImage( nullptr ), m_SelectedImageMask( nullptr ), m_SelectedPlanarFigure( nullptr ), m_ImageObserverTag( -1 ), m_ImageMaskObserverTag( -1 ), m_PlanarFigureObserverTag( -1 ), m_TimeObserverTag( -1 ), m_CurrentStatisticsValid( false ), m_StatisticsUpdatePending( false ), m_DataNodeSelectionChanged ( false ), m_Visible(false) { this->m_CalculationThread = new QmitkImageStatisticsCalculationThread; } QmitkImageStatisticsView::~QmitkImageStatisticsView() { if ( m_SelectedImage != nullptr ) m_SelectedImage->RemoveObserver( m_ImageObserverTag ); if ( m_SelectedImageMask != nullptr ) m_SelectedImageMask->RemoveObserver( m_ImageMaskObserverTag ); if ( m_SelectedPlanarFigure != nullptr ) m_SelectedPlanarFigure->RemoveObserver( m_PlanarFigureObserverTag ); while(this->m_CalculationThread->isRunning()) // wait until thread has finished { itksys::SystemTools::Delay(100); } delete this->m_CalculationThread; } void QmitkImageStatisticsView::CreateQtPartControl(QWidget *parent) { if (m_Controls == nullptr) { m_Controls = new Ui::QmitkImageStatisticsViewControls; m_Controls->setupUi(parent); CreateConnections(); m_Controls->m_ErrorMessageLabel->hide(); m_Controls->m_StatisticsWidgetStack->setCurrentIndex(0); m_Controls->m_BinSizeFrame->setEnabled(false); #if QT_VERSION < QT_VERSION_CHECK(5, 10, 0) m_Controls->m_StatisticsWidgetStack->setVisible(false); m_Controls->label_HistogramIsInvisibleWarning->setEnabled(true); m_Controls->label_HistogramIsInvisibleWarning->setVisible(true); m_Controls->label_HistogramIsInvisibleWarning->setText("Histogram is not visible because Qt 5.10 is required. You can use the button Copy to Clipboard below to retrieve values."); m_Controls->groupBox_plot->setVisible(false); #else m_Controls->label_HistogramIsInvisibleWarning->setVisible(false); #endif } } void QmitkImageStatisticsView::OnPageSuccessfullyLoaded() { berry::IPreferencesService* prefService = berry::WorkbenchPlugin::GetDefault()->GetPreferencesService(); m_StylePref = prefService->GetSystemPreferences()->Node(berry::QtPreferences::QT_STYLES_NODE); QString styleName = m_StylePref->Get(berry::QtPreferences::QT_STYLE_NAME, ""); if (styleName == ":/org.blueberry.ui.qt/darkstyle.qss") { this->m_Controls->m_JSHistogram->SetTheme(QmitkChartWidget::ChartStyle::darkstyle); } else { this->m_Controls->m_JSHistogram->SetTheme(QmitkChartWidget::ChartStyle::lightstyle); } } void QmitkImageStatisticsView::CreateConnections() { if ( m_Controls ) { connect( (QObject*)(this->m_Controls->m_ButtonCopyHistogramToClipboard), SIGNAL(clicked()),(QObject*) this, SLOT(OnClipboardHistogramButtonClicked()) ); connect( (QObject*)(this->m_Controls->m_ButtonCopyStatisticsToClipboard), SIGNAL(clicked()),(QObject*) this, SLOT(OnClipboardStatisticsButtonClicked()) ); connect( (QObject*)(this->m_Controls->m_IgnoreZerosCheckbox), SIGNAL(clicked()),(QObject*) this, SLOT(OnIgnoreZerosCheckboxClicked()) ); connect( (QObject*) this->m_CalculationThread, SIGNAL(finished()),this, SLOT( OnThreadedStatisticsCalculationEnds()),Qt::QueuedConnection); connect( (QObject*) this, SIGNAL(StatisticsUpdate()),this, SLOT( RequestStatisticsUpdate()), Qt::QueuedConnection); connect( (QObject*) this->m_Controls->m_StatisticsTable, SIGNAL(cellDoubleClicked(int,int)),this, SLOT( JumpToCoordinates(int,int)) ); connect((QObject*)(this->m_Controls->m_barRadioButton), SIGNAL(clicked()), (QObject*)(this), SLOT(OnBarRadioButtonSelected())); connect((QObject*)(this->m_Controls->m_lineRadioButton), SIGNAL(clicked()), (QObject*)(this), SLOT(OnLineRadioButtonSelected())); connect( (QObject*) (this->m_Controls->m_HistogramNBinsSpinbox), SIGNAL(editingFinished()), this, SLOT(OnHistogramNBinsCheckBoxValueChanged())); connect((QObject*)(this->m_Controls->m_UseDefaultNBinsCheckBox), SIGNAL(clicked()), (QObject*) this, SLOT(OnDefaultNBinsSpinBoxChanged())); connect((QObject*)(this->m_Controls->m_ShowSubchartCheckBox), SIGNAL(clicked()), (QObject*) this, SLOT(OnShowSubchartBoxChanged())); connect((QObject*)(this->m_Controls->m_JSHistogram), SIGNAL(PageSuccessfullyLoaded()), (QObject*) this, SLOT(OnPageSuccessfullyLoaded())); } } void QmitkImageStatisticsView::OnDefaultNBinsSpinBoxChanged() { if (this->m_Controls->m_UseDefaultNBinsCheckBox->isChecked()) { m_Controls->m_HistogramNBinsSpinbox->setValue(100); this->m_CalculationThread->SetHistogramNBins(m_Controls->m_HistogramNBinsSpinbox->value()); m_HistogramNBins = m_Controls->m_HistogramNBinsSpinbox->value(); } m_Controls->m_BinSizeFrame->setEnabled(!m_Controls->m_UseDefaultNBinsCheckBox->isChecked()); this->UpdateStatistics(); } void QmitkImageStatisticsView::OnShowSubchartBoxChanged() { bool showSubchart = this->m_Controls->m_ShowSubchartCheckBox->isChecked(); this->m_Controls->m_JSHistogram->Reload(showSubchart); } void QmitkImageStatisticsView::OnBarRadioButtonSelected() { this->m_Controls->m_JSHistogram->SetChartTypeForAllDataAndReload(QmitkChartWidget::ChartType::bar); } void QmitkImageStatisticsView::OnLineRadioButtonSelected() { this->m_Controls->m_JSHistogram->SetChartTypeForAllDataAndReload(QmitkChartWidget::ChartType::line); } void QmitkImageStatisticsView::PartClosed(const berry::IWorkbenchPartReference::Pointer& ) { } void QmitkImageStatisticsView::OnTimeChanged(const itk::EventObject& e) { if (this->m_SelectedDataNodes.isEmpty() || this->m_SelectedImage == nullptr) return; const mitk::SliceNavigationController::GeometryTimeEvent* timeEvent = dynamic_cast(&e); assert(timeEvent != nullptr); int timestep = timeEvent->GetPos(); if (this->m_SelectedImage->GetTimeSteps() > 1) { for (int x = 0; x < this->m_Controls->m_StatisticsTable->columnCount(); x++) { for (int y = 0; y < this->m_Controls->m_StatisticsTable->rowCount(); y++) { QTableWidgetItem* item = this->m_Controls->m_StatisticsTable->item(y, x); if (item == nullptr) break; if (x == timestep) { item->setBackgroundColor(Qt::yellow); } else { if (y % 2 == 0) item->setBackground(this->m_Controls->m_StatisticsTable->palette().base()); else item->setBackground(this->m_Controls->m_StatisticsTable->palette().alternateBase()); } } } this->m_Controls->m_StatisticsTable->viewport()->update(); } if ((this->m_SelectedImage->GetTimeSteps() == 1 && timestep == 0) || this->m_SelectedImage->GetTimeSteps() > 1) { // display histogram for selected timestep //bug in Qt thats leads to crash in debug builds. Fixed in Qt 5.10 #if QT_VERSION >= QT_VERSION_CHECK(5, 10, 0) m_Controls->m_JSHistogram->Clear(); #endif QmitkImageStatisticsCalculationThread::HistogramType::ConstPointer histogram = (QmitkImageStatisticsCalculationThread::HistogramType::ConstPointer)this->m_CalculationThread->GetTimeStepHistogram(timestep); if (histogram.IsNotNull()) { bool closedFigure = this->m_CalculationThread->GetStatisticsUpdateSuccessFlag(); if (closedFigure) { auto imageNameLabel = m_Controls->m_SelectedFeatureImageLabel->text().toStdString(); this->m_Controls->m_JSHistogram->AddData2D(ConvertHistogramToMap(histogram), imageNameLabel); if (this->m_Controls->m_lineRadioButton->isChecked()) { this->m_Controls->m_JSHistogram->SetChartType(imageNameLabel, QmitkChartWidget::ChartType::line); } else { this->m_Controls->m_JSHistogram->SetChartType(imageNameLabel, QmitkChartWidget::ChartType::bar); } this->m_Controls->m_JSHistogram->SetXAxisLabel("Grey value"); this->m_Controls->m_JSHistogram->SetYAxisLabel("Frequency"); this->m_Controls->m_JSHistogram->Show(this->m_Controls->m_ShowSubchartCheckBox->isChecked()); } } } } void QmitkImageStatisticsView::JumpToCoordinates(int row ,int col) { if(m_SelectedDataNodes.isEmpty()) { MITK_WARN("QmitkImageStatisticsView") << "No data node selected for statistics calculation." ; return; } mitk::Point3D world; if (row==5 && !m_WorldMinList.empty()) world = m_WorldMinList[col]; else if (row==4 && !m_WorldMaxList.empty()) world = m_WorldMaxList[col]; else return; mitk::IRenderWindowPart* part = this->GetRenderWindowPart(); if (part) { part->GetQmitkRenderWindow("axial")->GetSliceNavigationController()->SelectSliceByPoint(world); part->GetQmitkRenderWindow("sagittal")->GetSliceNavigationController()->SelectSliceByPoint(world); part->GetQmitkRenderWindow("coronal")->GetSliceNavigationController()->SelectSliceByPoint(world); mitk::SliceNavigationController::GeometryTimeEvent timeEvent(this->m_SelectedImage->GetTimeGeometry(), col); part->GetQmitkRenderWindow("axial")->GetSliceNavigationController()->SetGeometryTime(timeEvent); } } void QmitkImageStatisticsView::OnIgnoreZerosCheckboxClicked() { emit StatisticsUpdate(); } void QmitkImageStatisticsView::OnClipboardHistogramButtonClicked() { if (!m_CurrentStatisticsValid) { QApplication::clipboard()->clear(); } if (m_SelectedPlanarFigure == nullptr) { const unsigned int t = this->GetRenderWindowPart()->GetTimeNavigationController()->GetTime()->GetPos(); typedef mitk::ImageStatisticsCalculator::HistogramType HistogramType; const HistogramType *histogram = this->m_CalculationThread->GetTimeStepHistogram(t).GetPointer(); QString clipboard("Measurement \t Frequency\n"); for (HistogramType::ConstIterator it = histogram->Begin(); it != histogram->End(); ++it) { clipboard = clipboard.append("%L1 \t %L2\n") .arg(it.GetMeasurementVector()[0], 0, 'f', 2) .arg(it.GetFrequency()); } QApplication::clipboard()->setText( clipboard, QClipboard::Clipboard); } //If a (non-closed) PlanarFigure is selected, display a line profile widget else if (m_SelectedPlanarFigure != nullptr) { QString clipboard("Pixel \t Intensity\n"); for (unsigned int i = 0; i < m_IntensityProfileList.size(); i++) { clipboard = clipboard.append("%L1 \t %L2\n").arg(QString::number(i)).arg(QString::number(m_IntensityProfileList.at(i))); } QApplication::clipboard()->setText(clipboard, QClipboard::Clipboard); } } void QmitkImageStatisticsView::OnClipboardStatisticsButtonClicked() { QLocale tempLocal; QLocale::setDefault(QLocale(QLocale::English, QLocale::UnitedStates)); if ( m_CurrentStatisticsValid && !( m_SelectedPlanarFigure != nullptr)) { - const std::vector &statistics = + const std::vector &statistics = this->m_CalculationThread->GetStatisticsData(); // Set time borders for for loop ;) unsigned int startT, endT; if(this->m_Controls->m_CheckBox4dCompleteTable->checkState()==Qt::CheckState::Unchecked) { startT = this->GetRenderWindowPart()->GetTimeNavigationController()->GetTime()-> GetPos(); endT = startT+1; } else { startT = 0; endT = statistics.size(); } QVector< QVector > statisticsTable; QStringList headline{ "Timestep", "Mean", "Median", "StdDev", "RMS", "Max", "Min", "NumberOfVoxels", "Skewness", "Kurtosis", "Uniformity", "Entropy", "MPP", "UPP", "V [mm³]" }; for(int i=0;i row; row.append(headline.at(i)); statisticsTable.append(row); } // Fill Table for(unsigned int t=startT;tGetMean()) << QString::number(statistics[t]->GetMedian()) << QString::number(statistics[t]->GetStd()) << QString::number(statistics[t]->GetRMS()) << QString::number(statistics[t]->GetMax()) << QString::number(statistics[t]->GetMin()) << QString::number(statistics[t]->GetN()) << QString::number(statistics[t]->GetSkewness()) << QString::number(statistics[t]->GetKurtosis()) << QString::number(statistics[t]->GetUniformity()) << QString::number(statistics[t]->GetEntropy()) << QString::number(statistics[t]->GetMPP()) << QString::number(statistics[t]->GetUPP()) << QString::number(m_Controls->m_StatisticsTable->item(7, 0)->data(Qt::DisplayRole).toDouble()); for(int z=0;zsetText(clipboard, QClipboard::Clipboard); } else { QApplication::clipboard()->clear(); } QLocale::setDefault(tempLocal); } void QmitkImageStatisticsView::OnSelectionChanged( berry::IWorkbenchPart::Pointer /*part*/, const QList &nodes ) { if (this->m_Visible) { this->SelectionChanged( nodes ); } else { this->m_DataNodeSelectionChanged = true; } } void QmitkImageStatisticsView::SelectionChanged(const QList &selectedNodes) { //Clear Histogram if data node is deselected //bug in Qt thats leads to crash in debug builds. Fixed in Qt 5.10 #if QT_VERSION >= QT_VERSION_CHECK(5, 10, 0) m_Controls->m_JSHistogram->Clear(); #endif if( this->m_StatisticsUpdatePending ) { this->m_DataNodeSelectionChanged = true; return; // not ready for new data now! } if (selectedNodes.size() == this->m_SelectedDataNodes.size()) { int i = 0; for (; i < selectedNodes.size(); ++i) { if (selectedNodes.at(i) != this->m_SelectedDataNodes.at(i)) { break; } } // node selection did not change if (i == selectedNodes.size()) return; } //reset the feature image and image mask field m_Controls->m_SelectedFeatureImageLabel->setText("None"); m_Controls->m_SelectedMaskLabel->setText("None"); this->ReinitData(); if (selectedNodes.isEmpty()) { DisableHistogramGUIElements(); } else { EnableHistogramGUIElements(); ResetHistogramGUIElementsToDefault(); } if(selectedNodes.size() == 1 || selectedNodes.size() == 2) { bool isBinary = false; selectedNodes.value(0)->GetBoolProperty("binary",isBinary); mitk::NodePredicateDataType::Pointer isLabelSet = mitk::NodePredicateDataType::New("LabelSetImage"); isBinary |= isLabelSet->CheckNode(selectedNodes.value(0)); if(isBinary) { EnableHistogramGUIElements(); m_Controls->m_InfoLabel->setText(""); } for (int i= 0; i< selectedNodes.size(); ++i) { this->m_SelectedDataNodes.push_back(selectedNodes.at(i)); } this->m_DataNodeSelectionChanged = false; this->m_Controls->m_ErrorMessageLabel->setText( "" ); this->m_Controls->m_ErrorMessageLabel->hide(); emit StatisticsUpdate(); } else { this->m_DataNodeSelectionChanged = false; } } void QmitkImageStatisticsView::DisableHistogramGUIElements() { m_Controls->m_InfoLabel->setText(""); m_Controls->groupBox_histogram->setEnabled(false); m_Controls->groupBox_statistics->setEnabled(false); } void QmitkImageStatisticsView::ResetHistogramGUIElementsToDefault() { m_Controls->m_barRadioButton->setChecked(true); m_Controls->m_HistogramNBinsSpinbox->setValue(100); m_HistogramNBins = m_Controls->m_HistogramNBinsSpinbox->value(); m_Controls->m_UseDefaultNBinsCheckBox->setChecked(true); m_Controls->m_ShowSubchartCheckBox->setChecked(true); m_Controls->m_BinSizeFrame->setEnabled(false); m_Controls->m_barRadioButton->setEnabled(true); m_Controls->m_lineRadioButton->setEnabled(true); m_Controls->m_HistogramNBinsSpinbox->setEnabled(true); this->m_CalculationThread->SetHistogramNBins(m_Controls->m_HistogramNBinsSpinbox->value()); } void QmitkImageStatisticsView::EnableHistogramGUIElements() { m_Controls->groupBox_histogram->setEnabled(true); m_Controls->groupBox_plot->setEnabled(true); m_Controls->groupBox_statistics->setEnabled(true); } void QmitkImageStatisticsView::ReinitData() { while( this->m_CalculationThread->isRunning()) // wait until thread has finished { itksys::SystemTools::Delay(100); } if(this->m_SelectedImage != nullptr) { this->m_SelectedImage->RemoveObserver( this->m_ImageObserverTag); this->m_SelectedImage = nullptr; } if(this->m_SelectedImageMask != nullptr) { this->m_SelectedImageMask->RemoveObserver( this->m_ImageMaskObserverTag); this->m_SelectedImageMask = nullptr; } if(this->m_SelectedPlanarFigure != nullptr) { this->m_SelectedPlanarFigure->RemoveObserver( this->m_PlanarFigureObserverTag); this->m_SelectedPlanarFigure = nullptr; } this->m_SelectedDataNodes.clear(); this->m_StatisticsUpdatePending = false; m_Controls->m_ErrorMessageLabel->setText( "" ); m_Controls->m_ErrorMessageLabel->hide(); this->InvalidateStatisticsTableView(); m_Controls->m_StatisticsWidgetStack->setCurrentIndex( 0 ); } void QmitkImageStatisticsView::OnThreadedStatisticsCalculationEnds() { m_Controls->m_ErrorMessageLabel->setText(""); m_Controls->m_ErrorMessageLabel->hide(); this->WriteStatisticsToGUI(); } void QmitkImageStatisticsView::UpdateStatistics() { mitk::IRenderWindowPart* renderPart = this->GetRenderWindowPart(); if ( renderPart == nullptr ) { this->m_StatisticsUpdatePending = false; return; } m_WorldMinList.clear(); m_WorldMaxList.clear(); // classify selected nodes mitk::NodePredicateDataType::Pointer isImage = mitk::NodePredicateDataType::New("Image"); mitk::NodePredicateDataType::Pointer isLabelSet = mitk::NodePredicateDataType::New("LabelSetImage"); mitk::NodePredicateOr::Pointer imagePredicate = mitk::NodePredicateOr::New(isImage, isLabelSet); std::string maskName; std::string maskType; std::string featureImageName; unsigned int maskDimension = 0; // reset data from last run ITKCommandType::Pointer changeListener = ITKCommandType::New(); changeListener->SetCallbackFunction( this, &QmitkImageStatisticsView::SelectedDataModified ); mitk::DataNode::Pointer planarFigureNode; for( int i= 0 ; i < this->m_SelectedDataNodes.size(); ++i) { mitk::PlanarFigure::Pointer planarFig = dynamic_cast(this->m_SelectedDataNodes.at(i)->GetData()); if( imagePredicate->CheckNode(this->m_SelectedDataNodes.at(i)) ) { bool isMask = false; this->m_SelectedDataNodes.at(i)->GetPropertyValue("binary", isMask); isMask |= isLabelSet->CheckNode(this->m_SelectedDataNodes.at(i)); if( this->m_SelectedImageMask == nullptr && isMask) { this->m_SelectedImageMask = dynamic_cast(this->m_SelectedDataNodes.at(i)->GetData()); this->m_ImageMaskObserverTag = this->m_SelectedImageMask->AddObserver(itk::ModifiedEvent(), changeListener); maskName = this->m_SelectedDataNodes.at(i)->GetName(); maskType = m_SelectedImageMask->GetNameOfClass(); maskDimension = 3; } else if( !isMask ) { if(this->m_SelectedImage == nullptr) { this->m_SelectedImage = static_cast(this->m_SelectedDataNodes.at(i)->GetData()); this->m_ImageObserverTag = this->m_SelectedImage->AddObserver(itk::ModifiedEvent(), changeListener); } featureImageName = this->m_SelectedDataNodes.at(i)->GetName(); } } else if (planarFig.IsNotNull()) { if(this->m_SelectedPlanarFigure == nullptr) { this->m_SelectedPlanarFigure = planarFig; this->m_PlanarFigureObserverTag = this->m_SelectedPlanarFigure->AddObserver(mitk::EndInteractionPlanarFigureEvent(), changeListener); maskName = this->m_SelectedDataNodes.at(i)->GetName(); maskType = this->m_SelectedPlanarFigure->GetNameOfClass(); maskDimension = 2; planarFigureNode = m_SelectedDataNodes.at(i); } } else { m_Controls->m_ErrorMessageLabel->setText("Invalid data node type!"); m_Controls->m_ErrorMessageLabel->show(); } } if(maskName == "") { maskName = "None"; maskType = ""; maskDimension = 0; } if(featureImageName == "") { featureImageName = "None"; } if (m_SelectedPlanarFigure != nullptr && m_SelectedImage == nullptr) { mitk::DataStorage::SetOfObjects::ConstPointer parentSet = this->GetDataStorage()->GetSources(planarFigureNode); for (unsigned int i=0; iSize(); i++) { mitk::DataNode::Pointer node = parentSet->ElementAt(i); if( imagePredicate->CheckNode(node) ) { bool isMask = false; node->GetPropertyValue("binary", isMask); isMask |= isLabelSet->CheckNode(node); if( !isMask ) { if(this->m_SelectedImage == nullptr) { this->m_SelectedImage = static_cast(node->GetData()); this->m_ImageObserverTag = this->m_SelectedImage->AddObserver(itk::ModifiedEvent(), changeListener); } } } } } unsigned int timeStep = renderPart->GetTimeNavigationController()->GetTime()->GetPos(); if ( m_SelectedImage != nullptr && m_SelectedImage->IsInitialized()) { // Check if a the selected image is a multi-channel image. If yes, statistics // cannot be calculated currently. if ( m_SelectedImage->GetPixelType().GetNumberOfComponents() > 1 ) { m_Controls->m_ErrorMessageLabel->setText( "Multi-component images not supported." ); m_Controls->m_ErrorMessageLabel->show(); this->InvalidateStatisticsTableView(); m_Controls->m_StatisticsWidgetStack->setCurrentIndex( 0 ); m_CurrentStatisticsValid = false; this->m_StatisticsUpdatePending = false; this->DisableHistogramGUIElements(); m_Controls->m_InfoLabel->setText(""); return; } std::stringstream maskLabel; maskLabel << maskName; if ( maskDimension > 0 ) { maskLabel << " [" << maskDimension << "D " << maskType << "]"; } m_Controls->m_SelectedMaskLabel->setText( maskLabel.str().c_str() ); m_Controls->m_SelectedFeatureImageLabel->setText(featureImageName.c_str()); // check time step validity if(m_SelectedImage->GetDimension() <= 3 && timeStep > m_SelectedImage->GetDimension(3)-1) { timeStep = m_SelectedImage->GetDimension(3)-1; } // Add the used mask time step to the mask label so the user knows which mask time step was used // if the image time step is bigger than the total number of mask time steps (see // ImageStatisticsCalculator::ExtractImageAndMask) if (m_SelectedImageMask != nullptr) { unsigned int maskTimeStep = timeStep; if (maskTimeStep >= m_SelectedImageMask->GetTimeSteps()) { maskTimeStep = m_SelectedImageMask->GetTimeSteps() - 1; } m_Controls->m_SelectedMaskLabel->setText(m_Controls->m_SelectedMaskLabel->text() + QString(" (t=") + QString::number(maskTimeStep) + QString(")")); } // check if the segmentation mask is empty if (m_SelectedImageMask != NULL) { typedef itk::Image ItkImageType; typedef itk::ImageRegionConstIteratorWithIndex< ItkImageType > IteratorType; ItkImageType::Pointer itkImage; mitk::CastToItkImage( m_SelectedImageMask, itkImage ); bool empty = true; IteratorType it( itkImage, itkImage->GetLargestPossibleRegion() ); while ( !it.IsAtEnd() ) { ItkImageType::ValueType val = it.Get(); if ( val != 0 ) { empty = false; break; } ++it; } if ( empty ) { m_Controls->m_ErrorMessageLabel->setText( "Empty segmentation mask selected..." ); m_Controls->m_ErrorMessageLabel->show(); return; } } //// initialize thread and trigger it this->m_CalculationThread->SetIgnoreZeroValueVoxel( m_Controls->m_IgnoreZerosCheckbox->isChecked() ); this->m_CalculationThread->Initialize( m_SelectedImage, m_SelectedImageMask, m_SelectedPlanarFigure ); this->m_CalculationThread->SetTimeStep( timeStep ); m_Controls->m_ErrorMessageLabel->setText("Calculating statistics..."); m_Controls->m_ErrorMessageLabel->show(); try { // Compute statistics this->m_CalculationThread->start(); } catch ( const mitk::Exception& e) { m_Controls->m_ErrorMessageLabel->setText("" + QString(e.GetDescription()) + ""); m_Controls->m_ErrorMessageLabel->show(); this->m_StatisticsUpdatePending = false; } catch ( const std::runtime_error &e ) { // In case of exception, print error message on GUI m_Controls->m_ErrorMessageLabel->setText("" + QString(e.what()) + ""); m_Controls->m_ErrorMessageLabel->show(); this->m_StatisticsUpdatePending = false; } catch ( const std::exception &e ) { MITK_ERROR << "Caught exception: " << e.what(); // In case of exception, print error message on GUI m_Controls->m_ErrorMessageLabel->setText("" + QString(e.what()) + ""); m_Controls->m_ErrorMessageLabel->show(); this->m_StatisticsUpdatePending = false; } } else { this->m_StatisticsUpdatePending = false; } } void QmitkImageStatisticsView::SelectedDataModified() { if( !m_StatisticsUpdatePending ) { emit StatisticsUpdate(); } } void QmitkImageStatisticsView::NodeRemoved(const mitk::DataNode *node) { while(this->m_CalculationThread->isRunning()) // wait until thread has finished { itksys::SystemTools::Delay(100); } if (node->GetData() == m_SelectedImage) { m_SelectedImage = nullptr; } } void QmitkImageStatisticsView::RequestStatisticsUpdate() { if ( !m_StatisticsUpdatePending ) { if(this->m_DataNodeSelectionChanged) { this->SelectionChanged(this->GetCurrentSelection()); } else { this->m_StatisticsUpdatePending = true; this->UpdateStatistics(); } } if (this->GetRenderWindowPart()) this->GetRenderWindowPart()->RequestUpdate(); } void QmitkImageStatisticsView::OnHistogramNBinsCheckBoxValueChanged() { if (static_cast(m_Controls->m_HistogramNBinsSpinbox->value()) != m_HistogramNBins) { m_HistogramNBins = m_Controls->m_HistogramNBinsSpinbox->value(); this->m_CalculationThread->SetHistogramNBins(m_Controls->m_HistogramNBinsSpinbox->value()); this->UpdateStatistics(); } } void QmitkImageStatisticsView::WriteStatisticsToGUI() { //bug in Qt thats leads to crash in debug builds. Fixed in Qt 5.10 #if QT_VERSION >= QT_VERSION_CHECK(5, 10, 0) m_Controls->m_JSHistogram->Clear(); #endif m_IntensityProfileList.clear(); //Disconnect OnLineRadioButtonSelected() to prevent reloading chart when radiobutton is checked programmatically disconnect((QObject*)(this->m_Controls->m_JSHistogram), SIGNAL(PageSuccessfullyLoaded()), 0, 0); connect((QObject*)(this->m_Controls->m_JSHistogram), SIGNAL(PageSuccessfullyLoaded()), (QObject*) this, SLOT(OnPageSuccessfullyLoaded())); m_Controls->m_InfoLabel->setText(""); if (m_DataNodeSelectionChanged) { this->m_StatisticsUpdatePending = false; this->RequestStatisticsUpdate(); return; // stop visualization of results and calculate statistics of new selection } if (this->m_CalculationThread->GetStatisticsUpdateSuccessFlag()) { if (this->m_CalculationThread->GetStatisticsChangedFlag()) { // Do not show any error messages m_Controls->m_ErrorMessageLabel->hide(); m_CurrentStatisticsValid = true; } if (m_SelectedImage != nullptr) { //all statistics are now computed also on planar figures (lines, paths...)! // If a (non-closed) PlanarFigure is selected, display a line profile widget if (m_SelectedPlanarFigure != nullptr) { // Check if the (closed) planar figure is out of bounds and so no image mask could be calculated--> Intensity Profile can not be calculated bool outOfBounds = false; if (m_SelectedPlanarFigure->IsClosed() && m_SelectedImageMask == nullptr) { outOfBounds = true; const QString message("Planar figure is on a rotated image plane or outside the image bounds."); m_Controls->m_InfoLabel->setText(message); } // check whether PlanarFigure is initialized const mitk::PlaneGeometry *planarFigurePlaneGeometry = m_SelectedPlanarFigure->GetPlaneGeometry(); if (!(planarFigurePlaneGeometry == nullptr || outOfBounds)) { unsigned int timeStep = this->GetRenderWindowPart()->GetTimeNavigationController()->GetTime()->GetPos(); mitk::Image::Pointer image; if (this->m_CalculationThread->GetStatisticsImage()->GetDimension() == 4) { mitk::ImageTimeSelector::Pointer timeSelector = mitk::ImageTimeSelector::New(); timeSelector->SetInput(this->m_CalculationThread->GetStatisticsImage()); timeSelector->SetTimeNr(timeStep); timeSelector->Update(); image = timeSelector->GetOutput(); } else { image = this->m_CalculationThread->GetStatisticsImage(); } mitk::IntensityProfile::ConstPointer intensityProfile = (mitk::IntensityProfile::ConstPointer)mitk::ComputeIntensityProfile(image, m_SelectedPlanarFigure); m_IntensityProfileList = ConvertIntensityProfileToVector(intensityProfile); auto lineDataLabel = "Intensity profile " + m_Controls->m_SelectedMaskLabel->text().toStdString(); m_Controls->m_JSHistogram->SetChartType(lineDataLabel, QmitkChartWidget::ChartType::line); m_Controls->m_JSHistogram->AddData1D(m_IntensityProfileList, lineDataLabel); m_Controls->m_JSHistogram->SetXAxisLabel("Distance"); m_Controls->m_JSHistogram->SetYAxisLabel("Intensity"); m_Controls->m_JSHistogram->Show(m_Controls->m_ShowSubchartCheckBox->isChecked()); m_Controls->m_lineRadioButton->setChecked(true); m_Controls->m_lineRadioButton->setEnabled(false); m_Controls->m_barRadioButton->setEnabled(false); m_Controls->m_HistogramNBinsSpinbox->setEnabled(false); m_Controls->m_BinSizeFrame->setEnabled(false); m_Controls->m_UseDefaultNBinsCheckBox->setEnabled(false); //Reconnect OnLineRadioButtonSelected() connect((QObject*)(this->m_Controls->m_JSHistogram), SIGNAL(PageSuccessfullyLoaded()), (QObject*) this, SLOT(OnLineRadioButtonSelected())); auto statisticsVector = this->m_CalculationThread->GetStatisticsData(); //only one entry (current timestep) this->FillLinearProfileStatisticsTableView(statisticsVector.front().GetPointer(), this->m_CalculationThread->GetStatisticsImage()); QString message("Only linegraph available for an intensity profile!"); if (this->m_CalculationThread->GetStatisticsImage()->GetDimension() == 4) { message += "Only current timestep displayed!"; } message += ""; m_Controls->m_InfoLabel->setText(message); m_CurrentStatisticsValid = true; } else { // Clear statistics, histogram, and GUI this->InvalidateStatisticsTableView(); m_Controls->m_StatisticsWidgetStack->setCurrentIndex(0); m_CurrentStatisticsValid = false; m_Controls->m_ErrorMessageLabel->hide(); m_Controls->m_SelectedMaskLabel->setText("None"); this->m_StatisticsUpdatePending = false; if (!outOfBounds) m_Controls->m_InfoLabel->setText(""); return; } } else { m_Controls->m_StatisticsWidgetStack->setCurrentIndex(0); auto histogram = this->m_CalculationThread->GetTimeStepHistogram(this->m_CalculationThread->GetTimeStep()).GetPointer(); auto imageLabelName = m_Controls->m_SelectedFeatureImageLabel->text().toStdString(); m_Controls->m_JSHistogram->AddData2D(ConvertHistogramToMap(histogram), imageLabelName); m_Controls->m_JSHistogram->SetChartType(imageLabelName, QmitkChartWidget::ChartType::bar); this->m_Controls->m_JSHistogram->SetXAxisLabel("Gray value"); this->m_Controls->m_JSHistogram->SetYAxisLabel("Frequency"); m_Controls->m_UseDefaultNBinsCheckBox->setEnabled(true); m_Controls->m_JSHistogram->Show(this->m_Controls->m_ShowSubchartCheckBox->isChecked()); this->FillStatisticsTableView(this->m_CalculationThread->GetStatisticsData(), this->m_CalculationThread->GetStatisticsImage()); + + for (const auto& aStatistic: this->m_CalculationThread->GetStatisticsData()) { + auto statisticsNode = mitk::DataNode::New(); + statisticsNode->SetName(m_Controls->m_SelectedFeatureImageLabel->text().toStdString()); + statisticsNode->SetData(aStatistic); + statisticsNode->SetProperty("helper object", mitk::BoolProperty::New(true)); + this->GetDataStorage()->Add(statisticsNode); + } } m_CurrentStatisticsValid = true; } } else { m_Controls->m_SelectedMaskLabel->setText("None"); m_Controls->m_ErrorMessageLabel->setText(m_CalculationThread->GetLastErrorMessage().c_str()); m_Controls->m_ErrorMessageLabel->show(); // Clear statistics and histogram this->InvalidateStatisticsTableView(); m_Controls->m_StatisticsWidgetStack->setCurrentIndex(0); m_CurrentStatisticsValid = false; } berry::IPreferencesService* prefService = berry::WorkbenchPlugin::GetDefault()->GetPreferencesService(); m_StylePref = prefService->GetSystemPreferences()->Node(berry::QtPreferences::QT_STYLES_NODE); this->m_StatisticsUpdatePending = false; } void QmitkImageStatisticsView::FillStatisticsTableView( - const std::vector &statistics, + const std::vector &statistics, const mitk::Image *image ) { this->m_Controls->m_StatisticsTable->setColumnCount(image->GetTimeSteps()); this->m_Controls->m_StatisticsTable->horizontalHeader()->setVisible(image->GetTimeSteps() > 1); // Set Checkbox for complete copy of statistic table if(image->GetTimeSteps()>1) { this->m_Controls->m_CheckBox4dCompleteTable->setEnabled(true); } else { this->m_Controls->m_CheckBox4dCompleteTable->setEnabled(false); this->m_Controls->m_CheckBox4dCompleteTable->setChecked(false); } for (unsigned int t = 0; t < image->GetTimeSteps(); t++) { this->m_Controls->m_StatisticsTable->setHorizontalHeaderItem(t, new QTableWidgetItem(QString::number(t))); if (statistics.at(t)->GetMaxIndex().size()==3) { mitk::Point3D index, max, min; index[0] = statistics.at(t)->GetMaxIndex()[0]; index[1] = statistics.at(t)->GetMaxIndex()[1]; index[2] = statistics.at(t)->GetMaxIndex()[2]; m_SelectedImage->GetGeometry()->IndexToWorld(index, max); this->m_WorldMaxList.push_back(max); index[0] = statistics.at(t)->GetMinIndex()[0]; index[1] = statistics.at(t)->GetMinIndex()[1]; index[2] = statistics.at(t)->GetMinIndex()[2]; m_SelectedImage->GetGeometry()->IndexToWorld(index, min); this->m_WorldMinList.push_back(min); } auto statisticsVector = AssembleStatisticsIntoVector(statistics.at(t).GetPointer(), image); unsigned int count = 0; for (const auto& entry : statisticsVector) { auto item = new QTableWidgetItem(entry); this->m_Controls->m_StatisticsTable->setItem(count, t, item); count++; } } this->m_Controls->m_StatisticsTable->resizeColumnsToContents(); int height = STAT_TABLE_BASE_HEIGHT; if (this->m_Controls->m_StatisticsTable->horizontalHeader()->isVisible()) height += this->m_Controls->m_StatisticsTable->horizontalHeader()->height(); if (this->m_Controls->m_StatisticsTable->horizontalScrollBar()->isVisible()) height += this->m_Controls->m_StatisticsTable->horizontalScrollBar()->height(); this->m_Controls->m_StatisticsTable->setMinimumHeight(height); // make sure the current timestep's column is highlighted (and the correct histogram is displayed) unsigned int t = this->GetRenderWindowPart()->GetTimeNavigationController()->GetTime()-> GetPos(); mitk::SliceNavigationController::GeometryTimeEvent timeEvent(this->m_SelectedImage->GetTimeGeometry(), t); this->OnTimeChanged(timeEvent); t = std::min(image->GetTimeSteps() - 1, t); // See bug 18340 /*QString hotspotMean; hotspotMean.append(QString("%1").arg(s[t].GetHotspotStatistics().GetMean(), 0, 'f', decimals)); hotspotMean += " ("; for (int i=0; im_Controls->m_StatisticsTable->setItem( 7, t, new QTableWidgetItem( hotspotMean ) ); QString hotspotMax; hotspotMax.append(QString("%1").arg(s[t].GetHotspotStatistics().GetMax(), 0, 'f', decimals)); hotspotMax += " ("; for (int i=0; im_Controls->m_StatisticsTable->setItem( 8, t, new QTableWidgetItem( hotspotMax ) ); QString hotspotMin; hotspotMin.append(QString("%1").arg(s[t].GetHotspotStatistics().GetMin(), 0, 'f', decimals)); hotspotMin += " ("; for (int i=0; im_Controls->m_StatisticsTable->setItem( 9, t, new QTableWidgetItem( hotspotMin ) );*/ } -std::vector QmitkImageStatisticsView::AssembleStatisticsIntoVector(mitk::ImageStatisticsCalculator::StatisticsContainer::ConstPointer statistics, mitk::Image::ConstPointer image, bool noVolumeDefined) const +std::vector QmitkImageStatisticsView::AssembleStatisticsIntoVector(mitk::StatisticsContainer::ConstPointer statistics, mitk::Image::ConstPointer image, bool noVolumeDefined) const { std::vector result; unsigned int decimals = 2; //statistics of higher order should have 5 decimal places because they used to be very small unsigned int decimalsHigherOrderStatistics = 5; if (image->GetPixelType().GetComponentType() == itk::ImageIOBase::DOUBLE || image->GetPixelType().GetComponentType() == itk::ImageIOBase::FLOAT) { decimals = 5; } result.push_back(GetFormattedString(statistics->GetMean(), decimals)); result.push_back(GetFormattedString(statistics->GetMedian(), decimals)); result.push_back(GetFormattedString(statistics->GetStd(), decimals)); result.push_back(GetFormattedString(statistics->GetRMS(), decimals)); result.push_back(GetFormattedString(statistics->GetMax(), decimals) + " " + GetFormattedIndex(statistics->GetMaxIndex())); result.push_back(GetFormattedString(statistics->GetMin(), decimals) + " " + GetFormattedIndex(statistics->GetMinIndex())); //to prevent large negative values of empty image statistics if (statistics->GetN() != std::numeric_limits::min()) { result.push_back(GetFormattedString(statistics->GetN(), 0)); const mitk::BaseGeometry *geometry = image->GetGeometry(); if (geometry != NULL && !noVolumeDefined) { const mitk::Vector3D &spacing = image->GetGeometry()->GetSpacing(); double volume = spacing[0] * spacing[1] * spacing[2] * static_cast(statistics->GetN()); result.push_back(GetFormattedString(volume, decimals)); } else { result.push_back("NA"); } } else { result.push_back("NA"); result.push_back("NA"); } result.push_back(GetFormattedString(statistics->GetSkewness(), decimalsHigherOrderStatistics)); result.push_back(GetFormattedString(statistics->GetKurtosis(), decimalsHigherOrderStatistics)); result.push_back(GetFormattedString(statistics->GetUniformity(), decimalsHigherOrderStatistics)); result.push_back(GetFormattedString(statistics->GetEntropy(), decimalsHigherOrderStatistics)); result.push_back(GetFormattedString(statistics->GetMPP(), decimals)); result.push_back(GetFormattedString(statistics->GetUPP(), decimalsHigherOrderStatistics)); return result; } -void QmitkImageStatisticsView::FillLinearProfileStatisticsTableView(mitk::ImageStatisticsCalculator::StatisticsContainer::ConstPointer statistics, +void QmitkImageStatisticsView::FillLinearProfileStatisticsTableView(mitk::StatisticsContainer::ConstPointer statistics, const mitk::Image *image) { this->m_Controls->m_StatisticsTable->setColumnCount(1); this->m_Controls->m_StatisticsTable->horizontalHeader()->setVisible(false); m_PlanarFigureStatistics = this->AssembleStatisticsIntoVector(statistics, image, true); for (unsigned int i = 0; i< m_PlanarFigureStatistics.size(); i++) { this->m_Controls->m_StatisticsTable->setItem( i, 0, new QTableWidgetItem(m_PlanarFigureStatistics[i] )); } this->m_Controls->m_StatisticsTable->resizeColumnsToContents(); int height = STAT_TABLE_BASE_HEIGHT; if (this->m_Controls->m_StatisticsTable->horizontalHeader()->isVisible()) height += this->m_Controls->m_StatisticsTable->horizontalHeader()->height(); if (this->m_Controls->m_StatisticsTable->horizontalScrollBar()->isVisible()) height += this->m_Controls->m_StatisticsTable->horizontalScrollBar()->height(); this->m_Controls->m_StatisticsTable->setMinimumHeight(height); } void QmitkImageStatisticsView::InvalidateStatisticsTableView() { this->m_Controls->m_StatisticsTable->horizontalHeader()->setVisible(false); this->m_Controls->m_StatisticsTable->setColumnCount(1); for ( int i = 0; i < this->m_Controls->m_StatisticsTable->rowCount(); ++i ) { { this->m_Controls->m_StatisticsTable->setItem( i, 0, new QTableWidgetItem( "NA" ) ); } } this->m_Controls->m_StatisticsTable->setMinimumHeight(STAT_TABLE_BASE_HEIGHT); } void QmitkImageStatisticsView::Activated() { } void QmitkImageStatisticsView::Deactivated() { } void QmitkImageStatisticsView::Visible() { m_Visible = true; mitk::IRenderWindowPart* renderWindow = GetRenderWindowPart(); if (renderWindow) { itk::ReceptorMemberCommand::Pointer cmdTimeEvent = itk::ReceptorMemberCommand::New(); cmdTimeEvent->SetCallbackFunction(this, &QmitkImageStatisticsView::OnTimeChanged); // It is sufficient to add the observer to the axial render window since the GeometryTimeEvent // is always triggered by all views. m_TimeObserverTag = renderWindow->GetQmitkRenderWindow("axial")-> GetSliceNavigationController()-> AddObserver(mitk::SliceNavigationController::GeometryTimeEvent(nullptr, 0), cmdTimeEvent); } if (m_DataNodeSelectionChanged) { if (this->IsCurrentSelectionValid()) { this->SelectionChanged(this->GetCurrentSelection()); } else { this->SelectionChanged(this->GetDataManagerSelection()); } m_DataNodeSelectionChanged = false; } } void QmitkImageStatisticsView::Hidden() { m_Visible = false; // The slice navigation controller observer is removed here instead of in the destructor. // If it was called in the destructor, the application would freeze because the view's // destructor gets called after the render windows have been destructed. if ( m_TimeObserverTag != 0 ) { mitk::IRenderWindowPart* renderWindow = GetRenderWindowPart(); if (renderWindow) { renderWindow->GetQmitkRenderWindow("axial")->GetSliceNavigationController()-> RemoveObserver( m_TimeObserverTag ); } m_TimeObserverTag = 0; } } void QmitkImageStatisticsView::SetFocus() { } std::map QmitkImageStatisticsView::ConvertHistogramToMap(itk::Statistics::Histogram::ConstPointer histogram) const { std::map histogramMap; auto endIt = histogram->End(); auto it = histogram->Begin(); // generating Lists of measurement and frequencies for (; it != endIt; ++it) { double frequency = it.GetFrequency(); double measurement = it.GetMeasurementVector()[0]; histogramMap.emplace(measurement, frequency); } return histogramMap; } std::vector QmitkImageStatisticsView::ConvertIntensityProfileToVector(mitk::IntensityProfile::ConstPointer intensityProfile) const { std::vector intensityProfileList; auto end = intensityProfile->End(); for (auto it = intensityProfile->Begin(); it != end; ++it) { intensityProfileList.push_back(it.GetMeasurementVector()[0]); } return intensityProfileList; } QString QmitkImageStatisticsView::GetFormattedString(double value, unsigned int decimals) const { - typedef mitk::ImageStatisticsCalculator::StatisticsContainer::RealType RealType; + typedef mitk::StatisticsContainer::RealType RealType; RealType maxVal = std::numeric_limits::max(); if (value == maxVal) { return QString("NA"); } else { return QString("%1").arg(value, 0, 'f', decimals); } } QString QmitkImageStatisticsView::GetFormattedIndex(const vnl_vector& vector) const { if (vector.empty()) { return QString(); } QString formattedIndex("("); for (const auto& entry : vector) { formattedIndex += QString::number(entry); formattedIndex += ","; } formattedIndex.chop(1); formattedIndex += ")"; return formattedIndex; } diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.h b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.h index 8543970725..97c0ee1ed8 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.h +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/src/internal/QmitkImageStatisticsView.h @@ -1,191 +1,191 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef QmitkImageStatisticsView_H__INCLUDED #define QmitkImageStatisticsView_H__INCLUDED #include "ui_QmitkImageStatisticsViewControls.h" // Qmitk includes #include #include #include "QmitkImageStatisticsCalculationThread.h" #include // mitk includes #include #include #include #include /*! \brief QmitkImageStatisticsView is a bundle that allows statistics calculation from images. Three modes are supported: 1. Statistics of one image, 2. Statistics of an image and a segmentation, 3. Statistics of an image and a Planar Figure. The statistics calculation is realized in a separate thread to keep the gui accessible during calculation. \ingroup Plugins/org.mitk.gui.qt.measurementtoolbox */ class QmitkImageStatisticsView : public QmitkAbstractView, public mitk::ILifecycleAwarePart, public berry::IPartListener { Q_OBJECT private: /*! \ Convenient typedefs */ typedef QList SelectedDataNodeVectorType; typedef itk::SimpleMemberCommand< QmitkImageStatisticsView > ITKCommandType; std::map ConvertHistogramToMap(itk::Statistics::Histogram::ConstPointer histogram) const; std::vector ConvertIntensityProfileToVector(mitk::IntensityProfile::ConstPointer intensityProfile) const; - std::vector AssembleStatisticsIntoVector(mitk::ImageStatisticsCalculator::StatisticsContainer::ConstPointer statistics, mitk::Image::ConstPointer image, bool noVolumeDefined=false) const; + std::vector AssembleStatisticsIntoVector(mitk::StatisticsContainer::ConstPointer statistics, mitk::Image::ConstPointer image, bool noVolumeDefined=false) const; QString GetFormattedIndex(const vnl_vector& vector) const; QString GetFormattedString(double value, unsigned int decimals) const; public: /*! \brief default constructor */ QmitkImageStatisticsView(QObject *parent = nullptr, const char *name = nullptr); /*! \brief default destructor */ virtual ~QmitkImageStatisticsView(); /*! \brief method for creating the widget containing the application controls, like sliders, buttons etc. */ virtual void CreateQtPartControl(QWidget *parent) override; /*! \brief method for creating the connections of main and control widget */ virtual void CreateConnections(); /*! \brief Is called from the selection mechanism once the data manager selection has changed*/ void OnSelectionChanged(berry::IWorkbenchPart::Pointer part, const QList &selectedNodes) override; static const std::string VIEW_ID; static const int STAT_TABLE_BASE_HEIGHT; public slots: /** \brief Called when the statistics update is finished, sets the results to GUI.*/ void OnThreadedStatisticsCalculationEnds(); /** \brief Update bin size for histogram resolution. */ void OnHistogramNBinsCheckBoxValueChanged(); protected slots: /** \brief Saves the histogram to the clipboard */ void OnClipboardHistogramButtonClicked(); /** \brief Saves the statistics to the clipboard */ void OnClipboardStatisticsButtonClicked(); /** \brief Indicates if zeros should be excluded from statistics calculation */ void OnIgnoreZerosCheckboxClicked(); /** \brief Checks if update is possible and calls StatisticsUpdate() possible */ void RequestStatisticsUpdate(); /** \brief Jump to coordinates stored in the double clicked cell */ void JumpToCoordinates(int row, int col); /** \brief Toogle GUI elements if histogram default bin size checkbox value changed. */ void OnDefaultNBinsSpinBoxChanged(); void OnShowSubchartBoxChanged(); void OnBarRadioButtonSelected(); void OnLineRadioButtonSelected(); void OnPageSuccessfullyLoaded(); signals: /** \brief Method to set the data to the member and start the threaded statistics update */ void StatisticsUpdate(); protected: /** \brief Writes the calculated statistics to the GUI */ - void FillStatisticsTableView(const std::vector &statistics, + void FillStatisticsTableView(const std::vector &statistics, const mitk::Image *image); - void FillLinearProfileStatisticsTableView(mitk::ImageStatisticsCalculator::StatisticsContainer::ConstPointer statistics, const mitk::Image *image); + void FillLinearProfileStatisticsTableView(mitk::StatisticsContainer::ConstPointer statistics, const mitk::Image *image); /** \brief Removes statistics from the GUI */ void InvalidateStatisticsTableView(); /** \brief Recalculate statistics for currently selected image and mask and * update the GUI. */ void UpdateStatistics(); virtual void Activated() override; virtual void Deactivated() override; virtual void Visible() override; virtual void Hidden() override; virtual void SetFocus() override; /** \brief Method called when itkModifiedEvent is called by selected data. */ void SelectedDataModified(); /** \brief Method called when the data manager selection changes */ void SelectionChanged(const QList &selectedNodes); void DisableHistogramGUIElements(); void ResetHistogramGUIElementsToDefault(); void EnableHistogramGUIElements(); /** \brief Method called to remove old selection when a new selection is present */ void ReinitData(); /** \brief writes the statistics to the gui*/ void WriteStatisticsToGUI(); void NodeRemoved(const mitk::DataNode *node) override; /** \brief Is called right before the view closes (before the destructor) */ virtual void PartClosed(const berry::IWorkbenchPartReference::Pointer&) override; /** \brief Is called from the image navigator once the time step has changed */ void OnTimeChanged(const itk::EventObject&); /** \brief Required for berry::IPartListener */ virtual Events::Types GetPartEventTypes() const override { return Events::CLOSED; } // member variables Ui::QmitkImageStatisticsViewControls *m_Controls; // if you have a planar figure selected, the statistics values will be saved in this one. std::vector m_PlanarFigureStatistics; QmitkImageStatisticsCalculationThread* m_CalculationThread; // Image and mask data mitk::Image* m_SelectedImage; mitk::Image* m_SelectedImageMask; mitk::PlanarFigure* m_SelectedPlanarFigure; // observer tags long m_ImageObserverTag; long m_ImageMaskObserverTag; long m_PlanarFigureObserverTag; long m_TimeObserverTag; SelectedDataNodeVectorType m_SelectedDataNodes; bool m_CurrentStatisticsValid; bool m_StatisticsUpdatePending; bool m_DataNodeSelectionChanged; bool m_Visible; unsigned int m_HistogramNBins; std::vector m_WorldMinList; std::vector m_WorldMaxList; std::vector m_IntensityProfileList; berry::IPreferences::Pointer m_StylePref; }; #endif // QmitkImageStatisticsView_H__INCLUDED