diff --git a/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp b/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp index b55ebd8dbf..a283e650f9 100644 --- a/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp +++ b/Modules/ImageStatistics/Testing/mitkImageStatisticsCalculatorTest.cpp @@ -1,542 +1,762 @@ /*=================================================================== 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 /** * \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); CPPUNIT_TEST_SUITE_END(); public: void setUp() 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(); + void TestPic3DIgnorePixelValueMaskStatistics(); + void TestPic3DSecondaryMaskStatistics(); + + void TestUS4DCylStatistics_time0(); + void TestUS4DCylAxialPlanarFigureMaskStatistics_time0(); + void TestUS4DCylSagittalPlanarFigureMaskStatistics_time0(); + void TestUS4DCylCoronalPlanarFigureMaskStatistics_time0(); + void TestUS4DCylImageMaskStatistics_time0(); + void TestUS4DCylIgnorePixelValueMaskStatistics_time0(); + void TestUS4DCylSecondaryMaskStatistics_time0(); + + void TestUS4DCylStatistics_time1(); + void TestUS4DCylAxialPlanarFigureMaskStatistics_time1(); + void TestUS4DCylSagittalPlanarFigureMaskStatistics_time1(); + void TestUS4DCylCoronalPlanarFigureMaskStatistics_time1(); + void TestUS4DCylImageMaskStatistics_time1(); + void TestUS4DCylIgnorePixelValueMaskStatistics_time1(); + void TestUS4DCylSecondaryMaskStatistics_time1(); + + void TestDifferentNBinsForHistogramStatistics(); + void TestDifferentBinSizeForHistogramStatistic(); + + void TestSwitchFromBinSizeToNBins(); + void TestSwitchFromNBinsToBinSize(); + private: - mitk::Image::Pointer m_Image; + mitk::Image::Pointer m_TestImage; + + mitk::Image::Pointer m_Pic3DImage; + mitk::Image::Pointer m_Pic3DImageMask; + 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::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, mitk::PlanarFigure::Pointer polygon ); - // calculate statistics for the given image and planarpolygon + // calculate statistics for the given image and mask const mitk::ImageStatisticsCalculator::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, + int timeStep=0, + mitk::MaskGenerator::Pointer maskGen=nullptr, + mitk::MaskGenerator::Pointer secondardMaskGen=nullptr); + void VerifyStatistics(mitk::ImageStatisticsCalculator::StatisticsContainer::Pointer stats, double testMean, double testSD, double testMedian=0); + + void VerifyStatistics(mitk::ImageStatisticsCalculator::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::setUp() { - std::string filename = this->GetTestDataFilePath("ImageStatistics/testimage.dcm"); - if (filename.empty()) + std::string filename = this->GetTestDataFilePath("ImageStatistics/testimage.dcm"); + + std::string Pic3DFile = this->GetTestDataFilePath("Pic3D.nrrd"); + std::string Pic3DImageMaskFile = this->GetTestDataFilePath("ImageStatistics/Pic3DImageMask.nrrd"); + std::string Pic3DAxialPlanarFigureFile = this->GetTestDataFilePath("ImageStatistics/Pic3DAxialPlanarFigure.pf"); + std::string Pic3DSagittalPlanarFigureFile = this->GetTestDataFilePath("ImageStatistics/Pic3DSagittalPlanarFigure.pf"); + std::string Pic3DCoronalPlanarFigureFile = this->GetTestDataFilePath("ImageStatistics/Pic3DCoronalPlanarFigure.pf"); + + std::string US4DFile = this->GetTestDataFilePath("US4DCyl.nrrd"); + std::string US4DImageMaskFile = this->GetTestDataFilePath("ImageStatistics/US4DImageMask.nrrd"); + std::string US4DAxialPlanarFigureFile = this->GetTestDataFilePath("ImageStatistics/US4DAxialPlanarFigure.pf"); + std::string US4DSagittalPlanarFigureFile = this->GetTestDataFilePath("ImageStatistics/US4DSagittalPlanarFigure.pf"); + std::string US4DCoronalPlanarFigureFile = this->GetTestDataFilePath("ImageStatistics/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_Image = mitk::IOUtil::LoadImage(filename); - MITK_TEST_CONDITION_REQUIRED( m_Image.IsNotNull(), "Loaded an mitk::Image" ); + m_TestImage = mitk::IOUtil::LoadImage(filename); + 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 = mitk::IOUtil::LoadImage(Pic3DFile); + MITK_TEST_CONDITION_REQUIRED( m_Pic3DImage.IsNotNull(), "Loaded Pic3D" ); + m_Pic3DImageMask = mitk::IOUtil::LoadImage(Pic3DImageMaskFile); + MITK_TEST_CONDITION_REQUIRED( m_Pic3DImage.IsNotNull(), "Loaded Pic3D image 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 = mitk::IOUtil::LoadImage(US4DFile); + MITK_TEST_CONDITION_REQUIRED( m_US4DImage.IsNotNull(), "Loaded US4D" ); + m_US4DImageMask = mitk::IOUtil::LoadImage(US4DImageMaskFile); + MITK_TEST_CONDITION_REQUIRED( m_US4DImage.IsNotNull(), "Loaded US4D image mask" ); + 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" ); - m_Geometry = m_Image->GetSlicedGeometry()->GetPlaneGeometry(0); - MITK_TEST_CONDITION_REQUIRED( m_Geometry.IsNotNull(), "Getting image geometry" ) } 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_Image, figure1.GetPointer()), 255.0, 0.0, 255.0); + 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_Image, figure1.GetPointer()), 255.0, 0.0, 255.0); + 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_Image, figure1.GetPointer()), 255.0, 0.0, 255.0); + 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_Image, figure1.GetPointer()), 191.25, 110.41, 242.250); + 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_Image, figure1.GetPointer()), 191.50, 63.50, 134.340); + 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_Image, figure1.GetPointer()), 191.5, 63.50, 134.340); + 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_Image, figure1.GetPointer()), 127.66, 104.1, 140.250); + 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_Image, figure2.GetPointer()), 128.0, 0.0, 128.0); + 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_Image, figure2.GetPointer()), 191.5, 63.50, 134.340); + 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_Image, figure2.GetPointer()), 127.66, 104.1, 140.250); + 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_Image, figure2.GetPointer()), 204.0, 102.00, 242.250); + 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_Image, figure2.GetPointer()), 212.66, 59.860, 248.640); + 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_Image, 0 ); + mitk::Image::Pointer mask_image = mitk::ImageGenerator::GenerateImageFromReference( m_TestImage, 0 ); - this->VerifyStatistics( ComputeStatistics( m_Image, mask_image ), -21474836.480, -21474836.480, -21474836.480); // empty statisticsContainer (default values) + 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_Image, 0 ); + mitk::Image::Pointer mask_image = mitk::ImageGenerator::GenerateImageFromReference( m_TestImage, 0 ); 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(); // activate voxel in the mask image mitk::ImagePixelWriteAccessor< unsigned char, 3> writeAccess( mask_image ); while( indexIter != activated_indices.end() ) { writeAccess.SetPixelByIndex( (*indexIter++), 1); } - this->VerifyStatistics( ComputeStatistics( m_Image, mask_image ), 127.5, 127.5, 12.750); + 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_Image, 0 ); + mitk::Image::Pointer mask_image = mitk::ImageGenerator::GenerateImageFromReference( m_TestImage, 0 ); mitk::ImageStatisticsCalculator::Pointer statisticsCalculator = mitk::ImageStatisticsCalculator::New(); - statisticsCalculator->SetInputImage( m_Image ); + 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 itk::Index<3U> test_index = {11, 8, 0}; mitk::ImagePixelWriteAccessor< unsigned char, 3> writeAccess( mask_image ); writeAccess.SetPixelByIndex( test_index, 1); mask_image->Modified(); mitk::ImageStatisticsCalculator::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() +{ + long expected_N = 3211264; + double expected_mean = 0; + double expected_MPP = 0; + double expected_median = 0; + double expected_skewness = 0; + double expected_kurtosis = 0; + double expected_uniformity = 0; + double expected_UPP = 0; + double expected_variance = 0; + double expected_standarddev = 0; + double expected_min = 0; + double expected_max = 0; + double expected_RMS = 0; + double expected_entropy = 0; + 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(); + + 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 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(); } const mitk::ImageStatisticsCalculator::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 +mitkImageStatisticsCalculatorTestSuite::ComputeStatisticsNew(mitk::Image::Pointer image, + int timeStep, + mitk::MaskGenerator::Pointer maskGen, + mitk::MaskGenerator::Pointer secondardMaskGen) +{ + 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); +} void mitkImageStatisticsCalculatorTestSuite::VerifyStatistics(mitk::ImageStatisticsCalculator::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, + 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); + 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_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/mitkImageStatisticsCalculator.cpp b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp index 6c5c204c4f..c56ed54e59 100644 --- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp +++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp @@ -1,629 +1,634 @@ #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) { 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 (std::vector::iterator 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 (itk::ExceptionObject & e) { // 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()) { 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(); std::list::iterator 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); //typename ImageType::IndexType tmpMinIndex = minMaxFilter->GetMinIndex(*it); //typename ImageType::IndexType tmpMaxIndex = minMaxFilter->GetMaxIndex(*it); //minIndex.set_size(tmpMaxIndex.GetIndexDimension()); //maxIndex.set_size(tmpMaxIndex.GetIndexDimension()); 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] = tmpMinIndex[i] + (maskImage->GetOrigin()[i] - image->GetOrigin()[i]) / (double) maskImage->GetSpacing()[i]; //maxIndex[i] = tmpMaxIndex[i] + (maskImage->GetOrigin()[i] - image->GetOrigin()[i]) / (double) maskImage->GetSpacing()[i]; minIndex[i] = indexCoordinateMin[i]; maxIndex[i] = indexCoordinateMax[i]; } statisticsResult->SetMinIndex(minIndex); statisticsResult->SetMaxIndex(maxIndex); // just debug + TPixel min_Filter = minMaxFilter->GetMin(*it); + TPixel max_Filter = minMaxFilter->GetMax(*it); + TPixel min_Itk = imageStatisticsFilter->GetMinimum(*it); + TPixel max_Itk = imageStatisticsFilter->GetMaximum(*it); + assert(abs(minMaxFilter->GetMax(*it) - imageStatisticsFilter->GetMaximum(*it)) < mitk::eps); assert(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(std::numeric_limits::max()), m_Mean(std::numeric_limits::max()), m_Min(std::numeric_limits::min()), m_Max(std::numeric_limits::max()), m_Std(std::numeric_limits::max()), m_Variance(std::numeric_limits::max()), m_Skewness(std::numeric_limits::max()), m_Kurtosis(std::numeric_limits::max()), m_RMS(std::numeric_limits::max()), m_MPP(std::numeric_limits::max()), m_Median(std::numeric_limits::max()), m_Uniformity(std::numeric_limits::max()), m_UPP(std::numeric_limits::max()), m_Entropy(std::numeric_limits::max()) { 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 = std::numeric_limits::max(); m_Mean = std::numeric_limits::max(); m_Min = std::numeric_limits::max(); m_Max = std::numeric_limits::max(); m_Std = std::numeric_limits::max(); m_Variance = std::numeric_limits::max(); m_Skewness = std::numeric_limits::max(); m_Kurtosis = std::numeric_limits::max(); m_RMS = std::numeric_limits::max(); m_MPP = std::numeric_limits::max(); m_Median = std::numeric_limits::max(); m_Uniformity = std::numeric_limits::max(); m_UPP = std::numeric_limits::max(); m_Entropy = std::numeric_limits::max(); 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++) + 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++) + 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; } }