diff --git a/Modules/Classification/CLMiniApps/CLGlobalImageFeatures.cpp b/Modules/Classification/CLMiniApps/CLGlobalImageFeatures.cpp index e5b8aae5d9..aba0e521e7 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 + 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 + 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::GIFGreyLevelDistanceZone::Pointer gldzCalculator = mitk::GIFGreyLevelDistanceZone::New(); //Commented 2 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 + 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/src/GlobalImageFeatures/mitkGIFNeighbouringGreyLevelDependenceFeatures.cpp b/Modules/Classification/CLUtilities/src/GlobalImageFeatures/mitkGIFNeighbouringGreyLevelDependenceFeatures.cpp index 7ac3709f6f..3bce70d519 100644 --- a/Modules/Classification/CLUtilities/src/GlobalImageFeatures/mitkGIFNeighbouringGreyLevelDependenceFeatures.cpp +++ b/Modules/Classification/CLUtilities/src/GlobalImageFeatures/mitkGIFNeighbouringGreyLevelDependenceFeatures.cpp @@ -1,398 +1,398 @@ #include // MITK #include #include #include // ITK #include #include #include #include // STL #include static void MatrixFeaturesTo(mitk::NGLDMMatrixFeatures features, std::string prefix, mitk::GIFNeighbouringGreyLevelDependenceFeature::FeatureListType &featureList); mitk::NGLDMMatrixHolder::NGLDMMatrixHolder(double min, double max, int number, int depenence) : m_MinimumRange(min), m_MaximumRange(max), m_Stepsize(0), m_NumberOfDependences(depenence), m_NumberOfBins(number), m_NeighbourhoodSize(1), m_NumberOfNeighbourVoxels(0), m_NumberOfDependenceNeighbourVoxels(0), m_NumberOfNeighbourhoods(0), m_NumberOfCompleteNeighbourhoods(0) { m_Matrix.resize(number, depenence); m_Matrix.fill(0); m_Stepsize = (max - min) / (number); } int mitk::NGLDMMatrixHolder::IntensityToIndex(double intensity) { return std::floor((intensity - m_MinimumRange) / m_Stepsize); } double mitk::NGLDMMatrixHolder::IndexToMinIntensity(int index) { return m_MinimumRange + index * m_Stepsize; } double mitk::NGLDMMatrixHolder::IndexToMeanIntensity(int index) { return m_MinimumRange + (index+0.5) * m_Stepsize; } double mitk::NGLDMMatrixHolder::IndexToMaxIntensity(int index) { return m_MinimumRange + (index + 1) * m_Stepsize; } template void CalculateNGLDMMatrix(itk::Image* itkImage, itk::Image* mask, int alpha, int range, unsigned int direction, mitk::NGLDMMatrixHolder &holder) { typedef itk::Image ImageType; typedef itk::Image MaskImageType; typedef itk::NeighborhoodIterator ShapeIterType; typedef itk::NeighborhoodIterator ShapeMaskIterType; holder.m_NumberOfCompleteNeighbourhoods = 0; holder.m_NumberOfNeighbourhoods = 0; holder.m_NumberOfNeighbourVoxels = 0; holder.m_NumberOfDependenceNeighbourVoxels = 0; itk::Size radius; radius.Fill(range); if ((direction > 1) && (direction - 2 GetLargestPossibleRegion()); ShapeMaskIterType maskIter(radius, mask, mask->GetLargestPossibleRegion()); auto region = mask->GetLargestPossibleRegion(); auto center = imageIter.Size() / 2; auto iterSize = imageIter.Size(); holder.m_NeighbourhoodSize = iterSize-1; while (!maskIter.IsAtEnd()) { int sameValues = 0; bool completeNeighbourhood = true; int i = holder.IntensityToIndex(imageIter.GetCenterPixel()); if ((imageIter.GetCenterPixel() != imageIter.GetCenterPixel()) || (maskIter.GetCenterPixel() < 1)) { ++imageIter; ++maskIter; continue; } for (unsigned int position = 0; position < iterSize; ++position) { if (position == center) { continue; } if ( ! region.IsInside(maskIter.GetIndex(position))) { completeNeighbourhood = false; continue; } bool isInBounds; auto jIntensity = imageIter.GetPixel(position, isInBounds); auto jMask = maskIter.GetPixel(position, isInBounds); if (jMask < 1 || (jIntensity != jIntensity) || ( ! isInBounds)) { completeNeighbourhood = false; continue; } int j = holder.IntensityToIndex(jIntensity); holder.m_NumberOfNeighbourVoxels += 1; if (std::abs(i - j) <= alpha) { holder.m_NumberOfDependenceNeighbourVoxels += 1; ++sameValues; } } holder.m_Matrix(i, sameValues) += 1; holder.m_NumberOfNeighbourhoods += 1; if (completeNeighbourhood) { holder.m_NumberOfCompleteNeighbourhoods += 1; } ++imageIter; ++maskIter; } } void LocalCalculateFeatures( mitk::NGLDMMatrixHolder &holder, mitk::NGLDMMatrixFeatures & results ) { auto sijMatrix = holder.m_Matrix; auto piMatrix = holder.m_Matrix; auto pjMatrix = holder.m_Matrix; // double Ng = holder.m_NumberOfBins; // int NgSize = holder.m_NumberOfBins; double Ns = sijMatrix.sum(); piMatrix.rowwise().normalize(); pjMatrix.colwise().normalize(); for (int i = 0; i < holder.m_NumberOfBins; ++i) { double sj = 0; for (int j = 0; j < holder.m_NumberOfDependences; ++j) { double iInt = i+1 ;// holder.IndexToMeanIntensity(i); double sij = sijMatrix(i, j); double k = j + 1; double pij = sij / Ns; results.LowDependenceEmphasis += sij / k / k; results.HighDependenceEmphasis += sij * k*k; if (iInt != 0) { results.LowGreyLevelCountEmphasis += sij / iInt / iInt; } results.HighGreyLevelCountEmphasis += sij * iInt*iInt; if (iInt != 0) { results.LowDependenceLowGreyLevelEmphasis += sij / k / k / iInt / iInt; } results.LowDependenceHighGreyLevelEmphasis += sij * iInt*iInt / k / k; if (iInt != 0) { results.HighDependenceLowGreyLevelEmphasis += sij *k * k / iInt / iInt; } results.HighDependenceHighGreyLevelEmphasis += sij * k*k*iInt*iInt; results.MeanGreyLevelCount += iInt * pij; results.MeanDependenceCount += k * pij; if (pij > 0) { results.DependenceCountEntropy -= pij * std::log(pij) / std::log(2); } results.DependenceCountEnergy += pij*pij; sj += sij; } results.GreyLevelNonUniformity += sj*sj; results.GreyLevelNonUniformityNormalised += sj*sj; } for (int j = 0; j < holder.m_NumberOfDependences; ++j) { double si = 0; for (int i = 0; i < holder.m_NumberOfBins; ++i) { double sij = sijMatrix(i, j); si += sij; } results.DependenceCountNonUniformity += si*si; results.DependenceCountNonUniformityNormalised += si*si; } for (int i = 0; i < holder.m_NumberOfBins; ++i) { for (int j = 0; j < holder.m_NumberOfDependences; ++j) { double iInt = i + 1;// holder.IndexToMeanIntensity(i); double sij = sijMatrix(i, j); double k = j + 1; double pij = sij / Ns; results.GreyLevelVariance += (iInt - results.MeanGreyLevelCount)* (iInt - results.MeanGreyLevelCount) * pij; results.DependenceCountVariance += (k - results.MeanDependenceCount)* (k - results.MeanDependenceCount) * pij; } } results.LowDependenceEmphasis /= Ns; results.HighDependenceEmphasis /= Ns; results.LowGreyLevelCountEmphasis /= Ns; results.HighGreyLevelCountEmphasis /= Ns; results.LowDependenceLowGreyLevelEmphasis /= Ns; results.LowDependenceHighGreyLevelEmphasis /= Ns; results.HighDependenceLowGreyLevelEmphasis /= Ns; results.HighDependenceHighGreyLevelEmphasis /= Ns; results.GreyLevelNonUniformity /= Ns; results.GreyLevelNonUniformityNormalised /= (Ns*Ns); results.DependenceCountNonUniformity /= Ns; results.DependenceCountNonUniformityNormalised /= (Ns*Ns); results.DependenceCountPercentage = 1; results.ExpectedNeighbourhoodSize = holder.m_NeighbourhoodSize; results.AverageNeighbourhoodSize = holder.m_NumberOfNeighbourVoxels / (1.0 * holder.m_NumberOfNeighbourhoods); results.AverageIncompleteNeighbourhoodSize = (holder.m_NumberOfNeighbourVoxels - holder.m_NumberOfCompleteNeighbourhoods* holder.m_NeighbourhoodSize) / (1.0 * (holder.m_NumberOfNeighbourhoods - holder.m_NumberOfCompleteNeighbourhoods)); results.PercentageOfCompleteNeighbourhoods = (1.0*holder.m_NumberOfCompleteNeighbourhoods) / (1.0 * holder.m_NumberOfNeighbourhoods); results.PercentageOfDependenceNeighbours = holder.m_NumberOfDependenceNeighbourVoxels / (1.0 * holder.m_NumberOfNeighbourVoxels); } template void CalculateCoocurenceFeatures(itk::Image* itkImage, mitk::Image::Pointer mask, mitk::GIFNeighbouringGreyLevelDependenceFeature::FeatureListType & featureList, mitk::GIFNeighbouringGreyLevelDependenceFeature::GIFNeighbouringGreyLevelDependenceFeatureConfiguration config) { typedef itk::Image MaskType; double rangeMin = config.MinimumIntensity; double rangeMax = config.MaximumIntensity; int numberOfBins = config.Bins; typename MaskType::Pointer maskImage = MaskType::New(); mitk::CastToItkImage(mask, maskImage); std::vector resultVector; int numberofDependency = 37; if (VImageDimension == 2) numberofDependency = 37; mitk::NGLDMMatrixHolder holderOverall(rangeMin, rangeMax, numberOfBins, numberofDependency); mitk::NGLDMMatrixFeatures overallFeature; CalculateNGLDMMatrix(itkImage, maskImage, config.alpha, config.range, config.direction, holderOverall); LocalCalculateFeatures(holderOverall, overallFeature); MatrixFeaturesTo(overallFeature, config.FeatureEncoding, featureList); } static void MatrixFeaturesTo(mitk::NGLDMMatrixFeatures features, std::string prefix, mitk::GIFNeighbouringGreyLevelDependenceFeature::FeatureListType &featureList) { featureList.push_back(std::make_pair(prefix + "Low Dependence Emphasis", features.LowDependenceEmphasis)); featureList.push_back(std::make_pair(prefix + "High Dependence Emphasis", features.HighDependenceEmphasis)); featureList.push_back(std::make_pair(prefix + "Low Grey Level Count Emphasis", features.LowGreyLevelCountEmphasis)); featureList.push_back(std::make_pair(prefix + "High Grey Level Count Emphasis", features.HighGreyLevelCountEmphasis)); featureList.push_back(std::make_pair(prefix + "Low Dependence Low Grey Level Emphasis", features.LowDependenceLowGreyLevelEmphasis)); featureList.push_back(std::make_pair(prefix + "Low Dependence High Grey Level Emphasis", features.LowDependenceHighGreyLevelEmphasis)); featureList.push_back(std::make_pair(prefix + "High Dependence Low Grey Level Emphasis", features.HighDependenceLowGreyLevelEmphasis)); featureList.push_back(std::make_pair(prefix + "High Dependence High Grey Level Emphasis", features.HighDependenceHighGreyLevelEmphasis)); featureList.push_back(std::make_pair(prefix + "Grey Level Non-Uniformity", features.GreyLevelNonUniformity)); featureList.push_back(std::make_pair(prefix + "Grey Level Non-Uniformity Normalised", features.GreyLevelNonUniformityNormalised)); featureList.push_back(std::make_pair(prefix + "Dependence Count Non-Uniformity", features.DependenceCountNonUniformity)); - featureList.push_back(std::make_pair(prefix + "Dependence Count Non-Uniformtiy Normalised", features.DependenceCountNonUniformityNormalised)); + featureList.push_back(std::make_pair(prefix + "Dependence Count Non-Uniformity Normalised", features.DependenceCountNonUniformityNormalised)); featureList.push_back(std::make_pair(prefix + "Dependence Count Percentage", features.DependenceCountPercentage)); featureList.push_back(std::make_pair(prefix + "Grey Level Mean", features.MeanGreyLevelCount)); featureList.push_back(std::make_pair(prefix + "Grey Level Variance", features.GreyLevelVariance)); featureList.push_back(std::make_pair(prefix + "Dependence Count Mean", features.MeanDependenceCount)); featureList.push_back(std::make_pair(prefix + "Dependence Count Variance", features.DependenceCountVariance)); featureList.push_back(std::make_pair(prefix + "Dependence Count Entropy", features.DependenceCountEntropy)); featureList.push_back(std::make_pair(prefix + "Dependence Count Energy", features.DependenceCountEnergy)); featureList.push_back(std::make_pair(prefix + "Expected Neighbourhood Size", features.ExpectedNeighbourhoodSize)); featureList.push_back(std::make_pair(prefix + "Average Neighbourhood Size", features.AverageNeighbourhoodSize)); featureList.push_back(std::make_pair(prefix + "Average Incomplete Neighbourhood Size", features.AverageIncompleteNeighbourhoodSize)); featureList.push_back(std::make_pair(prefix + "Percentage of complete Neighbourhoods", features.PercentageOfCompleteNeighbourhoods)); featureList.push_back(std::make_pair(prefix + "Percentage of Dependence Neighbour Voxels", features.PercentageOfDependenceNeighbours)); } mitk::GIFNeighbouringGreyLevelDependenceFeature::GIFNeighbouringGreyLevelDependenceFeature() : m_Range(1.0) { SetShortName("ngld"); SetLongName("neighbouring-grey-level-dependence"); SetFeatureClassName("Neighbouring Grey Level Dependence"); } mitk::GIFNeighbouringGreyLevelDependenceFeature::FeatureListType mitk::GIFNeighbouringGreyLevelDependenceFeature::CalculateFeatures(const Image::Pointer & image, const Image::Pointer &mask) { FeatureListType featureList; InitializeQuantifier(image, mask); GIFNeighbouringGreyLevelDependenceFeatureConfiguration config; config.direction = GetDirection(); config.range = m_Range; config.alpha = 0; config.MinimumIntensity = GetQuantifier()->GetMinimum(); config.MaximumIntensity = GetQuantifier()->GetMaximum(); config.Bins = GetQuantifier()->GetBins(); config.FeatureEncoding = FeatureDescriptionPrefix(); AccessByItk_3(image, CalculateCoocurenceFeatures, mask, featureList,config); return featureList; } mitk::GIFNeighbouringGreyLevelDependenceFeature::FeatureNameListType mitk::GIFNeighbouringGreyLevelDependenceFeature::GetFeatureNames() { FeatureNameListType featureList; return featureList; } void mitk::GIFNeighbouringGreyLevelDependenceFeature::AddArguments(mitkCommandLineParser &parser) { std::string name = GetOptionPrefix(); parser.addArgument(GetLongName(), name, mitkCommandLineParser::Bool, "Calculate Neighbouring Grey Level Dependence Features", "Calculate Neighbouring grey level dependence based features", us::Any()); parser.addArgument(name + "::range", name + "::range", mitkCommandLineParser::String, "NGLD Range", "Define the range that is used (Semicolon-separated)", us::Any()); AddQuantifierArguments(parser); } void mitk::GIFNeighbouringGreyLevelDependenceFeature::CalculateFeaturesUsingParameters(const Image::Pointer & feature, const Image::Pointer &, const Image::Pointer &maskNoNAN, FeatureListType &featureList) { auto parsedArgs = GetParameter(); std::string name = GetOptionPrefix(); if (parsedArgs.count(GetLongName())) { std::vector ranges; if (parsedArgs.count(name + "::range")) { ranges = SplitDouble(parsedArgs[name + "::range"].ToString(), ';'); } else { ranges.push_back(1); } for (double range : ranges) { InitializeQuantifierFromParameters(feature, maskNoNAN); this->SetRange(range); MITK_INFO << "Start calculating NGLD"; auto localResults = this->CalculateFeatures(feature, maskNoNAN); featureList.insert(featureList.end(), localResults.begin(), localResults.end()); MITK_INFO << "Finished calculating NGLD"; } } } std::string mitk::GIFNeighbouringGreyLevelDependenceFeature::GetCurrentFeatureEncoding() { std::ostringstream ss; ss << m_Range; std::string strRange = ss.str(); return QuantifierParameterString() + "_Range-"+ss.str(); } diff --git a/Modules/Classification/CLUtilities/test/files.cmake b/Modules/Classification/CLUtilities/test/files.cmake index f2b6181da6..7f7004556f 100644 --- a/Modules/Classification/CLUtilities/test/files.cmake +++ b/Modules/Classification/CLUtilities/test/files.cmake @@ -1,11 +1,12 @@ set(MODULE_TESTS #mitkSmoothedClassProbabilitesTest.cpp mitkGIFCurvatureStatisticTest mitkGIFFirstOrderHistogramStatisticsTest mitkGIFImageDescriptionFeaturesTest mitkGIFLocalIntensityTest mitkGIFNeighbourhoodGreyToneDifferenceFeaturesTest + mitkGIFNeighbouringGreyLevelDependenceFeatureTest mitkGIFVolumetricDensityStatisticsTest mitkGIFVolumetricStatisticsTest #mitkGlobalFeaturesTest.cpp ) diff --git a/Modules/Classification/CLUtilities/test/mitkGIFNeighbouringGreyLevelDependenceFeatureTest.cpp b/Modules/Classification/CLUtilities/test/mitkGIFNeighbouringGreyLevelDependenceFeatureTest.cpp new file mode 100644 index 0000000000..d185f2730d --- /dev/null +++ b/Modules/Classification/CLUtilities/test/mitkGIFNeighbouringGreyLevelDependenceFeatureTest.cpp @@ -0,0 +1,155 @@ +/*=================================================================== + +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 mitkGIFNeighbouringGreyLevelDependenceFeatureTestSuite : public mitk::TestFixture +{ + CPPUNIT_TEST_SUITE(mitkGIFNeighbouringGreyLevelDependenceFeatureTestSuite ); + + 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::GIFNeighbouringGreyLevelDependenceFeature::Pointer featureCalculator = mitk::GIFNeighbouringGreyLevelDependenceFeature::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 24 features.", std::size_t(24), featureList.size()); + + // These values are obtained with IBSI + // Standard accuracy is 0.01 + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Dependence Emphasis with Large IBSI Phantom Image", 0.045, results["Neighbouring Grey Level Dependence::Low Dependence Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Dependence Emphasis with Large IBSI Phantom Image", 109, results["Neighbouring Grey Level Dependence::High Dependence Emphasis"], 1.0); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis with Large IBSI Phantom Image", 0.693, results["Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Grey Level Count Emphasis with Large IBSI Phantom Image", 7.66, results["Neighbouring Grey Level Dependence::High Grey Level Count Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 0.00963, results["Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 0.736, results["Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 102, results["Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 235, results["Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Non-Uniformity with Large IBSI Phantom Image", 37.9, results["Neighbouring Grey Level Dependence::Grey Level Non-Uniformity"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised with Large IBSI Phantom Image", 0.512, results["Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity with Large IBSI Phantom Image", 4.86, results["Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised with Large IBSI Phantom Image", 0.0657, results["Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Percentage with Large IBSI Phantom Image", 1, results["Neighbouring Grey Level Dependence::Dependence Count Percentage"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Variance with Large IBSI Phantom Image", 3.05, results["Neighbouring Grey Level Dependence::Grey Level Variance"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Variance with Large IBSI Phantom Image", 22.1, results["Neighbouring Grey Level Dependence::Dependence Count Variance"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Entropy with Large IBSI Phantom Image", 4.4, results["Neighbouring Grey Level Dependence::Dependence Count Entropy"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Energy with Large IBSI Phantom Image", 0.0533, results["Neighbouring Grey Level Dependence::Dependence Count Energy"], 0.01); + + // These values are obtained by manually running the tool + // Values might be wrong. + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Grey Level Mean with Large IBSI Phantom Image", 2.15, results["Neighbouring Grey Level Dependence::Grey Level Mean"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Dependence Count Mean with Large IBSI Phantom Image", 9.32, results["Neighbouring Grey Level Dependence::Dependence Count Mean"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Expected Neighbourhood Size with Large IBSI Phantom Image", 26, results["Neighbouring Grey Level Dependence::Expected Neighbourhood Size"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Average Neighbourhood Size with Large IBSI Phantom Image", 14.24, results["Neighbouring Grey Level Dependence::Average Neighbourhood Size"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size with Large IBSI Phantom Image", 14.24, results["Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods with Large IBSI Phantom Image", 0, results["Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels with Large IBSI Phantom Image", 0.584, results["Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels"], 0.01); + } + + void ImageDescription_PhantomTest_2D() + { + mitk::GIFNeighbouringGreyLevelDependenceFeature::Pointer featureCalculator = mitk::GIFNeighbouringGreyLevelDependenceFeature::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 144 features.", std::size_t(144), featureList.size()); + + // These values are obtained with IBSI + // Standard accuracy is 0.01 + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Emphasis with Large IBSI Phantom Image", 0.158, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Emphasis with Large IBSI Phantom Image", 19.2, results["SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Emphasis"], 1.0); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis with Large IBSI Phantom Image", 0.702, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Grey Level Count Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Grey Level Count Emphasis with Large IBSI Phantom Image", 7.49, results["SliceWise Mean Neighbouring Grey Level Dependence::High Grey Level Count Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 0.0473, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence Low Grey Level Emphasis"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 3.06, results["SliceWise Mean Neighbouring Grey Level Dependence::Low Dependence High Grey Level Emphasis"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis with Large IBSI Phantom Image", 17.6, results["SliceWise Mean Neighbouring Grey Level Dependence::High Dependence Low Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis with Large IBSI Phantom Image", 49.5, results["SliceWise Mean Neighbouring Grey Level Dependence::High Dependence High Grey Level Emphasis"], 1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity with Large IBSI Phantom Image", 10.2, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised with Large IBSI Phantom Image", 0.562, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Non-Uniformity Normalised"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity with Large IBSI Phantom Image", 3.96, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised with Large IBSI Phantom Image", 0.212, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Non-Uniformity Normalised"], 0.001); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Percentage with Large IBSI Phantom Image", 1, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Percentage"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Variance with Large IBSI Phantom Image", 2.7, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Variance"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Variance with Large IBSI Phantom Image", 2.73, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Variance"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Entropy with Large IBSI Phantom Image", 2.71, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Entropy"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Energy with Large IBSI Phantom Image", 0.17, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Energy"], 0.01); + + // These values are obtained by manually running the tool + // Values might be wrong. + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Mean with Large IBSI Phantom Image", 2.12, results["SliceWise Mean Neighbouring Grey Level Dependence::Grey Level Mean"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Mean with Large IBSI Phantom Image", 3.98, results["SliceWise Mean Neighbouring Grey Level Dependence::Dependence Count Mean"], 0.1); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Expected Neighbourhood Size with Large IBSI Phantom Image", 8, results["SliceWise Mean Neighbouring Grey Level Dependence::Expected Neighbourhood Size"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Average Neighbourhood Size with Large IBSI Phantom Image", 5.20, results["SliceWise Mean Neighbouring Grey Level Dependence::Average Neighbourhood Size"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size with Large IBSI Phantom Image", 4.5598, results["SliceWise Mean Neighbouring Grey Level Dependence::Average Incomplete Neighbourhood Size"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods with Large IBSI Phantom Image", 0.1831, results["SliceWise Mean Neighbouring Grey Level Dependence::Percentage of complete Neighbourhoods"], 0.01); + CPPUNIT_ASSERT_DOUBLES_EQUAL_MESSAGE("SliceWise Mean Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels with Large IBSI Phantom Image", 0.579, results["SliceWise Mean Neighbouring Grey Level Dependence::Percentage of Dependence Neighbour Voxels"], 0.01); + } + +}; + +MITK_TEST_SUITE_REGISTRATION(mitkGIFNeighbouringGreyLevelDependenceFeature ) \ No newline at end of file