diff --git a/Modules/Classification/CLMiniApps/CLGlobalImageFeatures.cpp b/Modules/Classification/CLMiniApps/CLGlobalImageFeatures.cpp index a8a32b8525..ce665572a6 100644 --- a/Modules/Classification/CLMiniApps/CLGlobalImageFeatures.cpp +++ b/Modules/Classification/CLMiniApps/CLGlobalImageFeatures.cpp @@ -1,758 +1,758 @@ /*=================================================================== 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 mitkCLPolyToNrrd_cpp #define mitkCLPolyToNrrd_cpp #include "time.h" #include #include #include #include "mitkCommandLineParser.h" #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 #include #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkResampleImageFilter.h" #include #include #include "QmitkRegisterClasses.h" #include "QmitkRenderWindow.h" #include "vtkRenderLargeImage.h" #include "vtkPNGWriter.h" typedef itk::Image< double, 3 > FloatImageType; typedef itk::Image< unsigned short, 3 > MaskImageType; template class punct_facet : public std::numpunct { public: punct_facet(charT sep) : m_Sep(sep) { } protected: charT do_decimal_point() const { return m_Sep; } private: charT m_Sep; }; template void ResampleImage(itk::Image* itkImage, float resolution, mitk::Image::Pointer& newImage) { typedef itk::Image ImageType; typedef itk::ResampleImageFilter ResampleFilterType; typename ResampleFilterType::Pointer resampler = ResampleFilterType::New(); auto spacing = itkImage->GetSpacing(); auto size = itkImage->GetLargestPossibleRegion().GetSize(); for (unsigned int i = 0; i < VImageDimension; ++i) { size[i] = size[i] / (1.0*resolution)*(1.0*spacing[i])+1.0; } spacing.Fill(resolution); resampler->SetInput(itkImage); resampler->SetSize(size); resampler->SetOutputSpacing(spacing); resampler->SetOutputOrigin(itkImage->GetOrigin()); resampler->SetOutputDirection(itkImage->GetDirection()); resampler->Update(); newImage->InitializeByItk(resampler->GetOutput()); mitk::GrabItkImageMemory(resampler->GetOutput(), newImage); } template static void CreateNoNaNMask(itk::Image* itkValue, mitk::Image::Pointer mask, mitk::Image::Pointer& newMask) { typedef itk::Image< TPixel, VImageDimension> LFloatImageType; typedef itk::Image< unsigned short, VImageDimension> LMaskImageType; typename LMaskImageType::Pointer itkMask = LMaskImageType::New(); mitk::CastToItkImage(mask, itkMask); typedef itk::ImageDuplicator< LMaskImageType > DuplicatorType; typename DuplicatorType::Pointer duplicator = DuplicatorType::New(); duplicator->SetInputImage(itkMask); duplicator->Update(); auto tmpMask = duplicator->GetOutput(); itk::ImageRegionIterator mask1Iter(itkMask, itkMask->GetLargestPossibleRegion()); itk::ImageRegionIterator mask2Iter(tmpMask, tmpMask->GetLargestPossibleRegion()); itk::ImageRegionIterator imageIter(itkValue, itkValue->GetLargestPossibleRegion()); while (!mask1Iter.IsAtEnd()) { mask2Iter.Set(0); if (mask1Iter.Value() > 0) { // Is not NaN if (imageIter.Value() == imageIter.Value()) { mask2Iter.Set(1); } } ++mask1Iter; ++mask2Iter; ++imageIter; } newMask->InitializeByItk(tmpMask); mitk::GrabItkImageMemory(tmpMask, newMask); } template static void ResampleMask(itk::Image* itkMoving, mitk::Image::Pointer ref, mitk::Image::Pointer& newMask) { typedef itk::Image< TPixel, VImageDimension> LMaskImageType; typedef itk::NearestNeighborInterpolateImageFunction< LMaskImageType> NearestNeighborInterpolateImageFunctionType; typedef itk::ResampleImageFilter ResampleFilterType; typename NearestNeighborInterpolateImageFunctionType::Pointer nn_interpolator = NearestNeighborInterpolateImageFunctionType::New(); typename LMaskImageType::Pointer itkRef = LMaskImageType::New(); mitk::CastToItkImage(ref, itkRef); typename ResampleFilterType::Pointer resampler = ResampleFilterType::New(); resampler->SetInput(itkMoving); resampler->SetReferenceImage(itkRef); resampler->UseReferenceImageOn(); resampler->SetInterpolator(nn_interpolator); resampler->Update(); newMask->InitializeByItk(resampler->GetOutput()); mitk::GrabItkImageMemory(resampler->GetOutput(), newMask); } static void ExtractSlicesFromImages(mitk::Image::Pointer image, mitk::Image::Pointer mask, mitk::Image::Pointer maskNoNaN, mitk::Image::Pointer morphMask, int direction, std::vector &imageVector, std::vector &maskVector, std::vector &maskNoNaNVector, std::vector &morphMaskVector) { typedef itk::Image< double, 2 > FloatImage2DType; typedef itk::Image< unsigned short, 2 > MaskImage2DType; FloatImageType::Pointer itkFloat = FloatImageType::New(); MaskImageType::Pointer itkMask = MaskImageType::New(); MaskImageType::Pointer itkMaskNoNaN = MaskImageType::New(); MaskImageType::Pointer itkMorphMask = MaskImageType::New(); mitk::CastToItkImage(mask, itkMask); mitk::CastToItkImage(maskNoNaN, itkMaskNoNaN); mitk::CastToItkImage(image, itkFloat); mitk::CastToItkImage(morphMask, itkMorphMask); int idxA, idxB, idxC; switch (direction) { case 0: idxA = 1; idxB = 2; idxC = 0; break; case 1: idxA = 0; idxB = 2; idxC = 1; break; case 2: idxA = 0; idxB = 1; idxC = 2; break; default: idxA = 1; idxB = 2; idxC = 0; break; } auto imageSize = image->GetLargestPossibleRegion().GetSize(); FloatImageType::IndexType index3D; FloatImage2DType::IndexType index2D; FloatImage2DType::SpacingType spacing2D; spacing2D[0] = itkFloat->GetSpacing()[idxA]; spacing2D[1] = itkFloat->GetSpacing()[idxB]; for (unsigned int i = 0; i < imageSize[idxC]; ++i) { FloatImage2DType::RegionType region; FloatImage2DType::IndexType start; FloatImage2DType::SizeType size; start[0] = 0; start[1] = 0; size[0] = imageSize[idxA]; size[1] = imageSize[idxB]; region.SetIndex(start); region.SetSize(size); FloatImage2DType::Pointer image2D = FloatImage2DType::New(); image2D->SetRegions(region); image2D->Allocate(); MaskImage2DType::Pointer mask2D = MaskImage2DType::New(); mask2D->SetRegions(region); mask2D->Allocate(); MaskImage2DType::Pointer masnNoNaN2D = MaskImage2DType::New(); masnNoNaN2D->SetRegions(region); masnNoNaN2D->Allocate(); MaskImage2DType::Pointer morph2D = MaskImage2DType::New(); morph2D->SetRegions(region); morph2D->Allocate(); unsigned long voxelsInMask = 0; for (unsigned int a = 0; a < imageSize[idxA]; ++a) { for (unsigned int b = 0; b < imageSize[idxB]; ++b) { index3D[idxA] = a; index3D[idxB] = b; index3D[idxC] = i; index2D[0] = a; index2D[1] = b; image2D->SetPixel(index2D, itkFloat->GetPixel(index3D)); mask2D->SetPixel(index2D, itkMask->GetPixel(index3D)); masnNoNaN2D->SetPixel(index2D, itkMaskNoNaN->GetPixel(index3D)); morph2D->SetPixel(index2D, itkMorphMask->GetPixel(index3D)); voxelsInMask += (itkMask->GetPixel(index3D) > 0) ? 1 : 0; } } image2D->SetSpacing(spacing2D); mask2D->SetSpacing(spacing2D); masnNoNaN2D->SetSpacing(spacing2D); morph2D->SetSpacing(spacing2D); mitk::Image::Pointer tmpFloatImage = mitk::Image::New(); tmpFloatImage->InitializeByItk(image2D.GetPointer()); mitk::GrabItkImageMemory(image2D, tmpFloatImage); mitk::Image::Pointer tmpMaskImage = mitk::Image::New(); tmpMaskImage->InitializeByItk(mask2D.GetPointer()); mitk::GrabItkImageMemory(mask2D, tmpMaskImage); mitk::Image::Pointer tmpMaskNoNaNImage = mitk::Image::New(); tmpMaskNoNaNImage->InitializeByItk(masnNoNaN2D.GetPointer()); mitk::GrabItkImageMemory(masnNoNaN2D, tmpMaskNoNaNImage); mitk::Image::Pointer tmpMorphMaskImage = mitk::Image::New(); tmpMorphMaskImage->InitializeByItk(morph2D.GetPointer()); mitk::GrabItkImageMemory(morph2D, tmpMorphMaskImage); if (voxelsInMask > 0) { imageVector.push_back(tmpFloatImage); maskVector.push_back(tmpMaskImage); maskNoNaNVector.push_back(tmpMaskNoNaNImage); morphMaskVector.push_back(tmpMorphMaskImage); } } } static void SaveSliceOrImageAsPNG(mitk::Image::Pointer image, mitk::Image::Pointer mask, std::string path, int index) { // Create a Standalone Datastorage for the single purpose of saving screenshots.. mitk::StandaloneDataStorage::Pointer ds = mitk::StandaloneDataStorage::New(); QmitkRenderWindow renderWindow; renderWindow.GetRenderer()->SetDataStorage(ds); auto nodeI = mitk::DataNode::New(); nodeI->SetData(image); auto nodeM = mitk::DataNode::New(); nodeM->SetData(mask); ds->Add(nodeI); ds->Add(nodeM); mitk::TimeGeometry::Pointer geo = ds->ComputeBoundingGeometry3D(ds->GetAll()); mitk::RenderingManager::GetInstance()->InitializeViews(geo); mitk::SliceNavigationController::Pointer sliceNaviController = renderWindow.GetSliceNavigationController(); unsigned int numberOfSteps = 1; if (sliceNaviController) { numberOfSteps = sliceNaviController->GetSlice()->GetSteps(); sliceNaviController->GetSlice()->SetPos(0); } renderWindow.show(); renderWindow.resize(256, 256); for (unsigned int currentStep = 0; currentStep < numberOfSteps; ++currentStep) { if (sliceNaviController) { sliceNaviController->GetSlice()->SetPos(currentStep); } renderWindow.GetRenderer()->PrepareRender(); vtkRenderWindow* renderWindow2 = renderWindow.GetVtkRenderWindow(); mitk::BaseRenderer* baserenderer = mitk::BaseRenderer::GetInstance(renderWindow2); auto vtkRender = baserenderer->GetVtkRenderer(); vtkRender->GetRenderWindow()->WaitForCompletion(); vtkRenderLargeImage* magnifier = vtkRenderLargeImage::New(); magnifier->SetInput(vtkRender); magnifier->SetMagnification(3.0); std::stringstream ss; ss << path << "_Idx-" << index << "_Step-"<> tmpImageName; auto fileWriter = vtkPNGWriter::New(); fileWriter->SetInputConnection(magnifier->GetOutputPort()); fileWriter->SetFileName(tmpImageName.c_str()); fileWriter->Write(); fileWriter->Delete(); } } int main(int argc, char* argv[]) { // Commented : Updated to a common interface, include, if possible, mask is type unsigned short, uses Quantification, Comments // Name follows standard scheme with Class Name::Feature Name // Commented 2: Updated to use automatic inclusion of list of parameters if required. mitk::GIFImageDescriptionFeatures::Pointer ipCalculator = mitk::GIFImageDescriptionFeatures::New(); // Commented 2, Tested mitk::GIFFirstOrderStatistics::Pointer firstOrderCalculator = mitk::GIFFirstOrderStatistics::New(); //Commented 2 mitk::GIFFirstOrderHistogramStatistics::Pointer firstOrderHistoCalculator = mitk::GIFFirstOrderHistogramStatistics::New(); // Commented 2, Tested mitk::GIFVolumetricStatistics::Pointer volCalculator = mitk::GIFVolumetricStatistics::New(); // Commented 2, Tested mitk::GIFVolumetricDensityStatistics::Pointer voldenCalculator = mitk::GIFVolumetricDensityStatistics::New(); // Commented 2, Tested mitk::GIFCooccurenceMatrix::Pointer coocCalculator = mitk::GIFCooccurenceMatrix::New(); // Commented 2, Will not be tested mitk::GIFCooccurenceMatrix2::Pointer cooc2Calculator = mitk::GIFCooccurenceMatrix2::New(); //Commented 2 mitk::GIFNeighbouringGreyLevelDependenceFeature::Pointer ngldCalculator = mitk::GIFNeighbouringGreyLevelDependenceFeature::New(); //Commented 2, Tested mitk::GIFGreyLevelRunLength::Pointer rlCalculator = mitk::GIFGreyLevelRunLength::New(); // Commented 2 - mitk::GIFGreyLevelSizeZone::Pointer glszCalculator = mitk::GIFGreyLevelSizeZone::New(); // Commented 2 + mitk::GIFGreyLevelSizeZone::Pointer glszCalculator = mitk::GIFGreyLevelSizeZone::New(); // Commented 2, Tested mitk::GIFGreyLevelDistanceZone::Pointer gldzCalculator = mitk::GIFGreyLevelDistanceZone::New(); //Commented 2, Tested mitk::GIFLocalIntensity::Pointer lociCalculator = mitk::GIFLocalIntensity::New(); //Commented 2, Tested mitk::GIFIntensityVolumeHistogramFeatures::Pointer ivohCalculator = mitk::GIFIntensityVolumeHistogramFeatures::New(); // Commented 2 mitk::GIFNeighbourhoodGreyToneDifferenceFeatures::Pointer ngtdCalculator = mitk::GIFNeighbourhoodGreyToneDifferenceFeatures::New(); //Commented 2, Tested mitk::GIFCurvatureStatistic::Pointer curvCalculator = mitk::GIFCurvatureStatistic::New(); //Commented 2, Tested std::vector features; features.push_back(volCalculator.GetPointer()); features.push_back(voldenCalculator.GetPointer()); features.push_back(curvCalculator.GetPointer()); features.push_back(firstOrderCalculator.GetPointer()); features.push_back(firstOrderHistoCalculator.GetPointer()); features.push_back(ivohCalculator.GetPointer()); features.push_back(lociCalculator.GetPointer()); features.push_back(coocCalculator.GetPointer()); features.push_back(cooc2Calculator.GetPointer()); features.push_back(ngldCalculator.GetPointer()); features.push_back(rlCalculator.GetPointer()); features.push_back(glszCalculator.GetPointer()); features.push_back(gldzCalculator.GetPointer()); features.push_back(ipCalculator.GetPointer()); features.push_back(ngtdCalculator.GetPointer()); mitkCommandLineParser parser; parser.setArgumentPrefix("--", "-"); mitk::cl::GlobalImageFeaturesParameter param; param.AddParameter(parser); parser.addArgument("--","-", mitkCommandLineParser::String, "---", "---", us::Any(),true); for (auto cFeature : features) { cFeature->AddArguments(parser); } parser.addArgument("--", "-", mitkCommandLineParser::String, "---", "---", us::Any(), true); parser.addArgument("description","d",mitkCommandLineParser::String,"Text","Description that is added to the output",us::Any()); parser.addArgument("direction", "dir", mitkCommandLineParser::String, "Int", "Allows to specify the direction for Cooc and RL. 0: All directions, 1: Only single direction (Test purpose), 2,3,4... Without dimension 0,1,2... ", us::Any()); parser.addArgument("slice-wise", "slice", mitkCommandLineParser::String, "Int", "Allows to specify if the image is processed slice-wise (number giving direction) ", us::Any()); parser.addArgument("output-mode", "omode", mitkCommandLineParser::Int, "Int", "Defines if the results of an image / slice are written in a single row (0 , default) or column (1)."); // Miniapp Infos parser.setCategory("Classification Tools"); parser.setTitle("Global Image Feature calculator"); parser.setDescription("Calculates different global statistics for a given segmentation / image combination"); parser.setContributor("MBI"); std::map parsedArgs = parser.parseArguments(argc, argv); param.ParseParameter(parsedArgs); if (parsedArgs.size()==0) { return EXIT_FAILURE; } if ( parsedArgs.count("help") || parsedArgs.count("h")) { return EXIT_SUCCESS; } //bool savePNGofSlices = true; //std::string folderForPNGOfSlices = "E:\\tmp\\bonekamp\\fig\\"; std::string version = "Version: 1.22"; MITK_INFO << version; std::ofstream log; if (param.useLogfile) { log.open(param.logfilePath, std::ios::app); log << version; log << "Image: " << param.imagePath; log << "Mask: " << param.maskPath; } if (param.useDecimalPoint) { std::cout.imbue(std::locale(std::cout.getloc(), new punct_facet(param.decimalPoint))); } mitk::Image::Pointer image; mitk::Image::Pointer mask; mitk::Image::Pointer tmpImage = mitk::IOUtil::LoadImage(param.imagePath); mitk::Image::Pointer tmpMask = mitk::IOUtil::LoadImage(param.maskPath); image = tmpImage; mask = tmpMask; mitk::Image::Pointer morphMask = mask; if (param.useMorphMask) { morphMask = mitk::IOUtil::LoadImage(param.morphPath); } log << " Check for Dimensions -"; if ((image->GetDimension() != mask->GetDimension())) { MITK_INFO << "Dimension of image does not match. "; MITK_INFO << "Correct one image, may affect the result"; if (image->GetDimension() == 2) { mitk::Convert2Dto3DImageFilter::Pointer multiFilter2 = mitk::Convert2Dto3DImageFilter::New(); multiFilter2->SetInput(tmpImage); multiFilter2->Update(); image = multiFilter2->GetOutput(); } if (mask->GetDimension() == 2) { mitk::Convert2Dto3DImageFilter::Pointer multiFilter3 = mitk::Convert2Dto3DImageFilter::New(); multiFilter3->SetInput(tmpMask); multiFilter3->Update(); mask = multiFilter3->GetOutput(); } } int writeDirection = 0; if (parsedArgs.count("output-mode")) { writeDirection = us::any_cast(parsedArgs["output-mode"]); } log << " Check for Resolution -"; if (param.resampleToFixIsotropic) { mitk::Image::Pointer newImage = mitk::Image::New(); AccessByItk_2(image, ResampleImage, param.resampleResolution, newImage); image = newImage; } if ( ! mitk::Equal(mask->GetGeometry(0)->GetOrigin(), image->GetGeometry(0)->GetOrigin())) { MITK_INFO << "Not equal Origins"; if (param.ensureSameSpace) { MITK_INFO << "Warning!"; MITK_INFO << "The origin of the input image and the mask do not match. They are"; MITK_INFO << "now corrected. Please check to make sure that the images still match"; image->GetGeometry(0)->SetOrigin(mask->GetGeometry(0)->GetOrigin()); } else { return -1; } } log << " Resample if required -"; if (param.resampleMask) { mitk::Image::Pointer newMaskImage = mitk::Image::New(); AccessByItk_2(mask, ResampleMask, image, newMaskImage); mask = newMaskImage; } log << " Check for Equality -"; if ( ! mitk::Equal(mask->GetGeometry(0)->GetSpacing(), image->GetGeometry(0)->GetSpacing())) { MITK_INFO << "Not equal Sapcings"; if (param.ensureSameSpace) { MITK_INFO << "Warning!"; MITK_INFO << "The spacing of the mask was set to match the spacing of the input image."; MITK_INFO << "This might cause unintended spacing of the mask image"; image->GetGeometry(0)->SetSpacing(mask->GetGeometry(0)->GetSpacing()); } else { MITK_INFO << "The spacing of the mask and the input images is not equal."; MITK_INFO << "Terminating the programm. You may use the '-fi' option"; return -1; } } int direction = 0; if (parsedArgs.count("direction")) { direction = mitk::cl::splitDouble(parsedArgs["direction"].ToString(), ';')[0]; } MITK_INFO << "Start creating Mask without NaN"; mitk::Image::Pointer maskNoNaN = mitk::Image::New(); AccessByItk_2(image, CreateNoNaNMask, mask, maskNoNaN); //CreateNoNaNMask(mask, image, maskNoNaN); bool sliceWise = false; int sliceDirection = 0; unsigned int currentSlice = 0; bool imageToProcess = true; std::vector floatVector; std::vector maskVector; std::vector maskNoNaNVector; std::vector morphMaskVector; if ((parsedArgs.count("slice-wise")) && image->GetDimension() > 2) { MITK_INFO << "Enabled slice-wise"; sliceWise = true; sliceDirection = mitk::cl::splitDouble(parsedArgs["slice-wise"].ToString(), ';')[0]; MITK_INFO << sliceDirection; ExtractSlicesFromImages(image, mask, maskNoNaN, morphMask, sliceDirection, floatVector, maskVector, maskNoNaNVector, morphMaskVector); MITK_INFO << "Slice"; } log << " Configure features -"; for (auto cFeature : features) { if (param.defineGlobalMinimumIntensity) { cFeature->SetMinimumIntensity(param.globalMinimumIntensity); cFeature->SetUseMinimumIntensity(true); } if (param.defineGlobalMaximumIntensity) { cFeature->SetMaximumIntensity(param.globalMaximumIntensity); cFeature->SetUseMaximumIntensity(true); } if (param.defineGlobalNumberOfBins) { cFeature->SetBins(param.globalNumberOfBins); MITK_INFO << param.globalNumberOfBins; } cFeature->SetParameter(parsedArgs); cFeature->SetDirection(direction); cFeature->SetEncodeParameters(param.encodeParameter); } bool addDescription = parsedArgs.count("description"); mitk::cl::FeatureResultWritter writer(param.outputPath, writeDirection); if (param.useDecimalPoint) { writer.SetDecimalPoint(param.decimalPoint); } std::string description = ""; if (addDescription) { description = parsedArgs["description"].ToString(); } mitk::Image::Pointer cImage = image; mitk::Image::Pointer cMask = mask; mitk::Image::Pointer cMaskNoNaN = maskNoNaN; mitk::Image::Pointer cMorphMask = morphMask; if (param.useHeader) { writer.AddColumn("SoftwareVersion"); writer.AddColumn("Patient"); writer.AddColumn("Image"); writer.AddColumn("Segmentation"); } // Create a QTApplication and a Datastorage // This is necessary in order to save screenshots of // each image / slice. QApplication qtapplication(argc, argv); QmitkRegisterClasses(); std::vector allStats; log << " Begin Processing -"; while (imageToProcess) { if (sliceWise) { cImage = floatVector[currentSlice]; cMask = maskVector[currentSlice]; cMaskNoNaN = maskNoNaNVector[currentSlice]; cMorphMask = morphMaskVector[currentSlice]; imageToProcess = (floatVector.size()-1 > (currentSlice)) ? true : false ; } else { imageToProcess = false; } if (param.writePNGScreenshots) { SaveSliceOrImageAsPNG(cImage, cMask, param.pngScreenshotsPath, currentSlice); } if (param.writeAnalysisImage) { mitk::IOUtil::Save(cImage, param.anaylsisImagePath); } if (param.writeAnalysisMask) { mitk::IOUtil::Save(cMask, param.analysisMaskPath); } mitk::AbstractGlobalImageFeature::FeatureListType stats; for (auto cFeature : features) { log << " Calculating " << cFeature->GetFeatureClassName() << " -"; cFeature->SetMorphMask(cMorphMask); cFeature->CalculateFeaturesUsingParameters(cImage, cMask, cMaskNoNaN, stats); } for (std::size_t i = 0; i < stats.size(); ++i) { std::cout << stats[i].first << " - " << stats[i].second << std::endl; } writer.AddHeader(description, currentSlice, stats, param.useHeader, addDescription); if (true) { writer.AddSubjectInformation(MITK_REVISION); writer.AddSubjectInformation(param.imageFolder); writer.AddSubjectInformation(param.imageName); writer.AddSubjectInformation(param.maskName); } writer.AddResult(description, currentSlice, stats, param.useHeader, addDescription); allStats.push_back(stats); ++currentSlice; } log << " Process Slicewise -"; if (sliceWise) { mitk::AbstractGlobalImageFeature::FeatureListType statMean, statStd; for (std::size_t i = 0; i < allStats[0].size(); ++i) { auto cElement1 = allStats[0][i]; cElement1.first = "SliceWise Mean " + cElement1.first; cElement1.second = 0.0; auto cElement2 = allStats[0][i]; cElement2.first = "SliceWise Var. " + cElement2.first; cElement2.second = 0.0; statMean.push_back(cElement1); statStd.push_back(cElement2); } for (auto cStat : allStats) { for (std::size_t i = 0; i < cStat.size(); ++i) { statMean[i].second += cStat[i].second / (1.0*allStats.size()); } } for (auto cStat : allStats) { for (std::size_t i = 0; i < cStat.size(); ++i) { statStd[i].second += (cStat[i].second - statMean[i].second)*(cStat[i].second - statMean[i].second) / (1.0*allStats.size()); } } for (std::size_t i = 0; i < statMean.size(); ++i) { std::cout << statMean[i].first << " - " << statMean[i].second << std::endl; std::cout << statStd[i].first << " - " << statStd[i].second << std::endl; } if (true) { writer.AddSubjectInformation(MITK_REVISION); writer.AddSubjectInformation(param.imageFolder); writer.AddSubjectInformation(param.imageName); writer.AddSubjectInformation(param.maskName + " - Mean"); } writer.AddResult(description, currentSlice, statMean, param.useHeader, addDescription); if (true) { writer.AddSubjectInformation(MITK_REVISION); writer.AddSubjectInformation(param.imageFolder); writer.AddSubjectInformation(param.imageName); writer.AddSubjectInformation(param.maskName + " - Var."); } writer.AddResult(description, currentSlice, statStd, param.useHeader, addDescription); } if (param.useLogfile) { log << "Finished calculation" << std::endl; log.close(); } return 0; } #endif diff --git a/Modules/Classification/CLUtilities/test/files.cmake b/Modules/Classification/CLUtilities/test/files.cmake index 25893e7d95..b0f32b97fe 100644 --- a/Modules/Classification/CLUtilities/test/files.cmake +++ b/Modules/Classification/CLUtilities/test/files.cmake @@ -1,13 +1,14 @@ set(MODULE_TESTS mitkGIFCurvatureStatisticTest mitkGIFFirstOrderHistogramStatisticsTest mitkGIFGreyLevelDistanceZoneTest + mitkGIFGreyLevelSizeZoneTest mitkGIFImageDescriptionFeaturesTest mitkGIFLocalIntensityTest mitkGIFNeighbourhoodGreyToneDifferenceFeaturesTest mitkGIFNeighbouringGreyLevelDependenceFeatureTest mitkGIFVolumetricDensityStatisticsTest mitkGIFVolumetricStatisticsTest #mitkSmoothedClassProbabilitesTest.cpp #mitkGlobalFeaturesTest.cpp ) diff --git a/Modules/Classification/CLUtilities/test/mitkGIFGreyLevelSizeZoneTest.cpp b/Modules/Classification/CLUtilities/test/mitkGIFGreyLevelSizeZoneTest.cpp new file mode 100644 index 0000000000..6c3d21dcec --- /dev/null +++ b/Modules/Classification/CLUtilities/test/mitkGIFGreyLevelSizeZoneTest.cpp @@ -0,0 +1,145 @@ +/*=================================================================== + +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 "mitkIOUtil.h" +#include + +#include + +class mitkGIFGreyLevelSizeZoneTestSuite : public mitk::TestFixture +{ + CPPUNIT_TEST_SUITE(mitkGIFGreyLevelSizeZoneTestSuite ); + + MITK_TEST(ImageDescription_PhantomTest_3D); + MITK_TEST(ImageDescription_PhantomTest_2D); + + CPPUNIT_TEST_SUITE_END(); + +private: + mitk::Image::Pointer m_IBSI_Phantom_Image_Small; + mitk::Image::Pointer m_IBSI_Phantom_Image_Large; + mitk::Image::Pointer m_IBSI_Phantom_Mask_Small; + mitk::Image::Pointer m_IBSI_Phantom_Mask_Large; + +public: + + void setUp(void) override + { + m_IBSI_Phantom_Image_Small = mitk::IOUtil::LoadImage(GetTestDataFilePath("Radiomics/IBSI_Phantom_Image_Small.nrrd")); + m_IBSI_Phantom_Image_Large = mitk::IOUtil::LoadImage(GetTestDataFilePath("Radiomics/IBSI_Phantom_Image_Large.nrrd")); + m_IBSI_Phantom_Mask_Small = mitk::IOUtil::LoadImage(GetTestDataFilePath("Radiomics/IBSI_Phantom_Mask_Small.nrrd")); + m_IBSI_Phantom_Mask_Large = mitk::IOUtil::LoadImage(GetTestDataFilePath("Radiomics/IBSI_Phantom_Mask_Large.nrrd")); + } + + void ImageDescription_PhantomTest_3D() + { + mitk::GIFGreyLevelSizeZone::Pointer featureCalculator = mitk::GIFGreyLevelSizeZone::New(); + + featureCalculator->SetUseBinsize(true); + featureCalculator->SetBinsize(1.0); + featureCalculator->SetUseMinimumIntensity(true); + featureCalculator->SetUseMaximumIntensity(true); + featureCalculator->SetMinimumIntensity(0.5); + featureCalculator->SetMaximumIntensity(6.5); + + auto featureList = featureCalculator->CalculateFeatures(m_IBSI_Phantom_Image_Large, m_IBSI_Phantom_Mask_Large); + + std::map results; + for (auto valuePair : featureList) + { + MITK_INFO << valuePair.first << " : " << valuePair.second; + results[valuePair.first] = valuePair.second; + } + CPPUNIT_ASSERT_EQUAL_MESSAGE("Image Diagnostics should calculate 18 features.", std::size_t(18), featureList.size()); + + // These values are obtained with IBSI + // Standard accuracy is 0.01 + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Small Zone Emphasis with Large IBSI Phantom Image", 0.255, results["Grey Level Size Zone::Small Zone Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Large Zone Emphasis with Large IBSI Phantom Image", 550, results["Grey Level Size Zone::Large Zone Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Low Grey Level Emphasis with Large IBSI Phantom Image", 0.253, results["Grey Level Size Zone::Low Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::High Grey Level Emphasis with Large IBSI Phantom Image", 15.6, results["Grey Level Size Zone::High Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Small Zone Low Grey Level Emphasis with Large IBSI Phantom Image", 0.0256, results["Grey Level Size Zone::Small Zone Low Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Small Zone High Grey Level Emphasis with Large IBSI Phantom Image", 2.76, results["Grey Level Size Zone::Small Zone High Grey Level Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Large Zone Low Grey Level Emphasis with Large IBSI Phantom Image", 503, results["Grey Level Size Zone::Large Zone Low Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Large Zone High Grey Level Emphasis with Large IBSI Phantom Image", 1495, results["Grey Level Size Zone::Large Zone High Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Grey Level Non-Uniformity with Large IBSI Phantom Image", 1.4, results["Grey Level Size Zone::Grey Level Non-Uniformity"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Grey Level Non-Uniformity Normalized with Large IBSI Phantom Image", 0.28, results["Grey Level Size Zone::Grey Level Non-Uniformity Normalized"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Zone Size Non-Uniformity with Large IBSI Phantom Image", 1, results["Grey Level Size Zone::Zone Size Non-Uniformity"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Zone Size Non-Uniformity Normalized with Large IBSI Phantom Image", 0.2, results["Grey Level Size Zone::Zone Size Non-Uniformity Normalized"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Zone Percentage with Large IBSI Phantom Image", 0.0676, results["Grey Level Size Zone::Zone Percentage"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Grey Level Variance with Large IBSI Phantom Image", 2.64, results["Grey Level Size Zone::Grey Level Variance"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Zone Size Variance with Large IBSI Phantom Image", 331, results["Grey Level Size Zone::Zone Size Variance"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Zone Size Entropy with Large IBSI Phantom Image", 2.32, results["Grey Level Size Zone::Zone Size Entropy"], 0.01); + //CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone:: with Large IBSI Phantom Image", 0.045, results["Grey Level Size Zone::"], 0.001); + + // These values are obtained by manually running the tool + // Values might be wrong. + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Grey Level Mean with Large IBSI Phantom Image", 3.6, results["Grey Level Size Zone::Grey Level Mean"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Grey Level Size Zone::Zone Size Mean with Large IBSI Phantom Image", 14.8, results["Grey Level Size Zone::Zone Size Mean"], 0.001); + } + + void ImageDescription_PhantomTest_2D() + { + mitk::GIFGreyLevelSizeZone::Pointer featureCalculator = mitk::GIFGreyLevelSizeZone::New(); + + featureCalculator->SetUseBinsize(true); + featureCalculator->SetBinsize(1.0); + featureCalculator->SetUseMinimumIntensity(true); + featureCalculator->SetUseMaximumIntensity(true); + featureCalculator->SetMinimumIntensity(0.5); + featureCalculator->SetMaximumIntensity(6.5); + + auto featureList = featureCalculator->CalculateFeaturesSlicewise(m_IBSI_Phantom_Image_Large, m_IBSI_Phantom_Mask_Large, 2); + + std::map results; + for (auto valuePair : featureList) + { + MITK_INFO << valuePair.first << " : " << valuePair.second; + results[valuePair.first] = valuePair.second; + } + CPPUNIT_ASSERT_EQUAL_MESSAGE("Image Diagnostics should calculate 108 features.", std::size_t(108), featureList.size()); + + // These values are obtained with IBSI + // Standard accuracy is 0.01 + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Small Zone Emphasis with Large IBSI Phantom Image", 0.363, results["SliceWise Mean Grey Level Size Zone::Small Zone Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Large Zone Emphasis with Large IBSI Phantom Image", 43.9, results["SliceWise Mean Grey Level Size Zone::Large Zone Emphasis"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Low Grey Level Emphasis with Large IBSI Phantom Image", 0.371, results["SliceWise Mean Grey Level Size Zone::Low Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::High Grey Level Emphasis with Large IBSI Phantom Image", 16.4, results["SliceWise Mean Grey Level Size Zone::High Grey Level Emphasis"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Small Zone Low Grey Level Emphasis with Large IBSI Phantom Image", 0.0259, results["SliceWise Mean Grey Level Size Zone::Small Zone Low Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Small Zone High Grey Level Emphasis with Large IBSI Phantom Image", 10.3, results["SliceWise Mean Grey Level Size Zone::Small Zone High Grey Level Emphasis"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Large Zone Low Grey Level Emphasis with Large IBSI Phantom Image", 40.4, results["SliceWise Mean Grey Level Size Zone::Large Zone Low Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Large Zone High Grey Level Emphasis with Large IBSI Phantom Image", 113, results["SliceWise Mean Grey Level Size Zone::Large Zone High Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Grey Level Non-Uniformity with Large IBSI Phantom Image", 1.41, results["SliceWise Mean Grey Level Size Zone::Grey Level Non-Uniformity"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Grey Level Non-Uniformity Normalized with Large IBSI Phantom Image", 0.323, results["SliceWise Mean Grey Level Size Zone::Grey Level Non-Uniformity Normalized"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Zone Size Non-Uniformity with Large IBSI Phantom Image", 1.49, results["SliceWise Mean Grey Level Size Zone::Zone Size Non-Uniformity"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Zone Size Non-Uniformity Normalized with Large IBSI Phantom Image", 0.333, results["SliceWise Mean Grey Level Size Zone::Zone Size Non-Uniformity Normalized"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Zone Percentage with Large IBSI Phantom Image", 0.24, results["SliceWise Mean Grey Level Size Zone::Zone Percentage"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Grey Level Variance with Large IBSI Phantom Image", 3.97, results["SliceWise Mean Grey Level Size Zone::Grey Level Variance"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Zone Size Variance with Large IBSI Phantom Image", 21, results["SliceWise Mean Grey Level Size Zone::Zone Size Variance"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Zone Size Entropy with Large IBSI Phantom Image", 1.93, results["SliceWise Mean Grey Level Size Zone::Zone Size Entropy"], 0.01); + //CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone:: with Large IBSI Phantom Image", 0.045, results["SliceWise Mean Grey Level Size Zone::"], 0.001); + + // These values are obtained by manually running the tool + // Values might be wrong. + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Grey Level Mean with Large IBSI Phantom Image", 3.526, results["SliceWise Mean Grey Level Size Zone::Grey Level Mean"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Grey Level Size Zone::Zone Size Mean with Large IBSI Phantom Image", 4.59524, results["SliceWise Mean Grey Level Size Zone::Zone Size Mean"], 0.001); + } + +}; + +MITK_TEST_SUITE_REGISTRATION(mitkGIFGreyLevelSizeZone ) \ No newline at end of file