diff --git a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp index 8158ccd21c..ae767c0e7e 100644 --- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp +++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp @@ -1,1877 +1,1879 @@ /*=================================================================== 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 "mitkImageAccessByItk.h" #include "mitkImageCast.h" #include "mitkExtractImageFilter.h" #include "mitkImageTimeSelector.h" #include "mitkITKImageImport.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 //#define DEBUG_HOTSPOTSEARCH #define _USE_MATH_DEFINES #include #if ( ( VTK_MAJOR_VERSION <= 5 ) && ( VTK_MINOR_VERSION<=8) ) #include "mitkvtkLassoStencilSource.h" #else #include "vtkLassoStencilSource.h" #endif // TODO DM: sort includes, check if they are really needed namespace mitk { ImageStatisticsCalculator::ImageStatisticsCalculator() : m_MaskingMode( MASKING_MODE_NONE ), m_MaskingModeChanged( false ), m_IgnorePixelValue(0.0), m_DoIgnorePixelValue(false), m_IgnorePixelValueChanged(false), m_PlanarFigureAxis (0), m_PlanarFigureSlice (0), m_PlanarFigureCoordinate0 (0), m_PlanarFigureCoordinate1 (0), // TODO DM: check order of variable initialization m_HotspotRadiusInMM(6.2035049089940), // radius of a 1cm3 sphere in mm m_CalculateHotspot(false), m_HotspotRadiusInMMChanged(false) { m_EmptyHistogram = HistogramType::New(); m_EmptyHistogram->SetMeasurementVectorSize(1); HistogramType::SizeType histogramSize(1); histogramSize.Fill( 256 ); m_EmptyHistogram->Initialize( histogramSize ); // m_EmptyStatistics.Reset(); } ImageStatisticsCalculator::~ImageStatisticsCalculator() { } ImageStatisticsCalculator::Statistics::Statistics() : m_Label(0), m_N(0), m_Min(0.0), m_Max(0.0), m_Median(0.0), m_Mean(0.0), m_Sigma(0.0), m_RMS(0.0), m_MaxIndex(1,-1), //TODO: Dimension anpassen m_MinIndex(1,-1), //TODO: Dimension anpassen m_HotspotN(0), m_HotspotMin(0.0), m_HotspotMax(0.0), m_HotspotMedian(0.0), m_HotspotMean(0.0), m_HotspotSigma(0.0), m_HotspotRMS(0.0), m_HotspotIndex(1,-1), //TODO: Dimension anpassen m_HotspotMaxIndex(1,-1), //TODO: Dimension anpassen m_HotspotMinIndex(1,-1) //TODO: Dimension anpassen { } ImageStatisticsCalculator::Statistics::~Statistics() { } //ImageStatisticsCalculator::Statistics::Reset() //{ // m_Label = 0; // m_N = 0; // m_Min = 0.0; // m_Max = 0.0; // m_Median = 0.0; // m_Mean = 0.0; // m_Sigma = 0.0; // m_RMS = 0.0; // m_MaxIndex = 0; // m_MinIndex = 0; // m_Label = 0; // m_HotspotN = 0; // m_HotspotMin = 0.0; // m_HotspotMax = 0.0; // m_HotspotMedian = 0.0; // m_HotspotMean = 0.0; // m_HotspotSigma = 0.0; // m_HotspotRMS = 0.0; // m_HotspotIndex = 0; // m_HotspotMaxIndex = 0; // m_HotspotMinIndex = 0; //} void ImageStatisticsCalculator::SetImage( const mitk::Image *image ) { if ( m_Image != image ) { m_Image = image; this->Modified(); unsigned int numberOfTimeSteps = image->GetTimeSteps(); // Initialize vectors to time-size of this image m_ImageHistogramVector.resize( numberOfTimeSteps ); m_MaskedImageHistogramVector.resize( numberOfTimeSteps ); m_PlanarFigureHistogramVector.resize( numberOfTimeSteps ); m_ImageStatisticsVector.resize( numberOfTimeSteps ); m_MaskedImageStatisticsVector.resize( numberOfTimeSteps ); m_PlanarFigureStatisticsVector.resize( numberOfTimeSteps ); m_ImageStatisticsTimeStampVector.resize( numberOfTimeSteps ); m_MaskedImageStatisticsTimeStampVector.resize( numberOfTimeSteps ); m_PlanarFigureStatisticsTimeStampVector.resize( numberOfTimeSteps ); m_ImageStatisticsCalculationTriggerVector.resize( numberOfTimeSteps ); m_MaskedImageStatisticsCalculationTriggerVector.resize( numberOfTimeSteps ); m_PlanarFigureStatisticsCalculationTriggerVector.resize( numberOfTimeSteps ); for ( unsigned int t = 0; t < image->GetTimeSteps(); ++t ) { m_ImageStatisticsTimeStampVector[t].Modified(); m_ImageStatisticsCalculationTriggerVector[t] = true; } } } void ImageStatisticsCalculator::SetImageMask( const mitk::Image *imageMask ) { if ( m_Image.IsNull() ) { itkExceptionMacro( << "Image needs to be set first!" ); } if ( m_Image->GetTimeSteps() != imageMask->GetTimeSteps() ) { itkExceptionMacro( << "Image and image mask need to have equal number of time steps!" ); } if ( m_ImageMask != imageMask ) { m_ImageMask = imageMask; this->Modified(); for ( unsigned int t = 0; t < m_Image->GetTimeSteps(); ++t ) { m_MaskedImageStatisticsTimeStampVector[t].Modified(); m_MaskedImageStatisticsCalculationTriggerVector[t] = true; } } } void ImageStatisticsCalculator::SetPlanarFigure( mitk::PlanarFigure *planarFigure ) { if ( m_Image.IsNull() ) { itkExceptionMacro( << "Image needs to be set first!" ); } if ( m_PlanarFigure != planarFigure ) { m_PlanarFigure = planarFigure; this->Modified(); for ( unsigned int t = 0; t < m_Image->GetTimeSteps(); ++t ) { m_PlanarFigureStatisticsTimeStampVector[t].Modified(); m_PlanarFigureStatisticsCalculationTriggerVector[t] = true; } } } void ImageStatisticsCalculator::SetMaskingMode( unsigned int mode ) { if ( m_MaskingMode != mode ) { m_MaskingMode = mode; m_MaskingModeChanged = true; this->Modified(); } } void ImageStatisticsCalculator::SetMaskingModeToNone() { if ( m_MaskingMode != MASKING_MODE_NONE ) { m_MaskingMode = MASKING_MODE_NONE; m_MaskingModeChanged = true; this->Modified(); } } void ImageStatisticsCalculator::SetMaskingModeToImage() { if ( m_MaskingMode != MASKING_MODE_IMAGE ) { m_MaskingMode = MASKING_MODE_IMAGE; m_MaskingModeChanged = true; this->Modified(); } } void ImageStatisticsCalculator::SetMaskingModeToPlanarFigure() { if ( m_MaskingMode != MASKING_MODE_PLANARFIGURE ) { m_MaskingMode = MASKING_MODE_PLANARFIGURE; m_MaskingModeChanged = true; this->Modified(); } } void ImageStatisticsCalculator::SetIgnorePixelValue(double value) { if ( m_IgnorePixelValue != value ) { m_IgnorePixelValue = value; if(m_DoIgnorePixelValue) { m_IgnorePixelValueChanged = true; } this->Modified(); } } double ImageStatisticsCalculator::GetIgnorePixelValue() { return m_IgnorePixelValue; } void ImageStatisticsCalculator::SetDoIgnorePixelValue(bool value) { if ( m_DoIgnorePixelValue != value ) { m_DoIgnorePixelValue = value; m_IgnorePixelValueChanged = true; this->Modified(); } } bool ImageStatisticsCalculator::GetDoIgnorePixelValue() { return m_DoIgnorePixelValue; } void ImageStatisticsCalculator::SetHotspotRadiusInMM(double value) { if ( m_HotspotRadiusInMM != value ) { m_HotspotRadiusInMM = value; if(m_CalculateHotspot) { m_HotspotRadiusInMMChanged = true; MITK_INFO <<"Hotspot radius changed, new convolution required"; } this->Modified(); } } double ImageStatisticsCalculator::GetHotspotRadiusInMM() { return m_HotspotRadiusInMM; } void ImageStatisticsCalculator::SetCalculateHotspot(bool on) { if ( m_CalculateHotspot != on ) { m_CalculateHotspot = on; m_HotspotRadiusInMMChanged = true; MITK_INFO <<"Hotspot calculation changed, new convolution required"; this->Modified(); } } bool ImageStatisticsCalculator::IsHotspotCalculated() { return m_CalculateHotspot; } bool ImageStatisticsCalculator::ComputeStatistics( unsigned int timeStep ) { if (m_Image.IsNull() ) { mitkThrow() << "Image not set!"; } if (!m_Image->IsInitialized()) { mitkThrow() << "Image not initialized!"; } if ( m_Image->GetReferenceCount() == 1 ) { // Image no longer valid; we are the only ones to still hold a reference on it return false; } if ( timeStep >= m_Image->GetTimeSteps() ) { throw std::runtime_error( "Error: invalid time step!" ); } // If a mask was set but we are the only ones to still hold a reference on // it, delete it. if ( m_ImageMask.IsNotNull() && (m_ImageMask->GetReferenceCount() == 1) ) { m_ImageMask = NULL; } // Check if statistics is already up-to-date unsigned long imageMTime = m_ImageStatisticsTimeStampVector[timeStep].GetMTime(); unsigned long maskedImageMTime = m_MaskedImageStatisticsTimeStampVector[timeStep].GetMTime(); unsigned long planarFigureMTime = m_PlanarFigureStatisticsTimeStampVector[timeStep].GetMTime(); bool imageStatisticsCalculationTrigger = m_ImageStatisticsCalculationTriggerVector[timeStep]; bool maskedImageStatisticsCalculationTrigger = m_MaskedImageStatisticsCalculationTriggerVector[timeStep]; bool planarFigureStatisticsCalculationTrigger = m_PlanarFigureStatisticsCalculationTriggerVector[timeStep]; if ( !m_IgnorePixelValueChanged && !m_HotspotRadiusInMMChanged && ((m_MaskingMode != MASKING_MODE_NONE) || (imageMTime > m_Image->GetMTime() && !imageStatisticsCalculationTrigger)) && ((m_MaskingMode != MASKING_MODE_IMAGE) || (maskedImageMTime > m_ImageMask->GetMTime() && !maskedImageStatisticsCalculationTrigger)) && ((m_MaskingMode != MASKING_MODE_PLANARFIGURE) || (planarFigureMTime > m_PlanarFigure->GetMTime() && !planarFigureStatisticsCalculationTrigger)) ) { // Statistics is up to date! if ( m_MaskingModeChanged ) { m_MaskingModeChanged = false; } else { return false; } } // Reset state changed flag m_MaskingModeChanged = false; m_IgnorePixelValueChanged = false; // Depending on masking mode, extract and/or generate the required image // and mask data from the user input this->ExtractImageAndMask( timeStep ); StatisticsContainer *statisticsContainer; HistogramContainer *histogramContainer; switch ( m_MaskingMode ) { case MASKING_MODE_NONE: default: if(!m_DoIgnorePixelValue) { statisticsContainer = &m_ImageStatisticsVector[timeStep]; histogramContainer = &m_ImageHistogramVector[timeStep]; m_ImageStatisticsTimeStampVector[timeStep].Modified(); m_ImageStatisticsCalculationTriggerVector[timeStep] = false; } else { statisticsContainer = &m_MaskedImageStatisticsVector[timeStep]; histogramContainer = &m_MaskedImageHistogramVector[timeStep]; m_MaskedImageStatisticsTimeStampVector[timeStep].Modified(); m_MaskedImageStatisticsCalculationTriggerVector[timeStep] = false; } break; case MASKING_MODE_IMAGE: statisticsContainer = &m_MaskedImageStatisticsVector[timeStep]; histogramContainer = &m_MaskedImageHistogramVector[timeStep]; m_MaskedImageStatisticsTimeStampVector[timeStep].Modified(); m_MaskedImageStatisticsCalculationTriggerVector[timeStep] = false; break; case MASKING_MODE_PLANARFIGURE: statisticsContainer = &m_PlanarFigureStatisticsVector[timeStep]; histogramContainer = &m_PlanarFigureHistogramVector[timeStep]; m_PlanarFigureStatisticsTimeStampVector[timeStep].Modified(); m_PlanarFigureStatisticsCalculationTriggerVector[timeStep] = false; break; } // Calculate statistics and histogram(s) if ( m_InternalImage->GetDimension() == 3 ) { if ( m_MaskingMode == MASKING_MODE_NONE && !m_DoIgnorePixelValue ) { AccessFixedDimensionByItk_2( m_InternalImage, InternalCalculateStatisticsUnmasked, 3, statisticsContainer, histogramContainer ); } else { AccessFixedDimensionByItk_3( m_InternalImage, InternalCalculateStatisticsMasked, 3, m_InternalImageMask3D.GetPointer(), statisticsContainer, histogramContainer ); } } else if ( m_InternalImage->GetDimension() == 2 ) { if ( m_MaskingMode == MASKING_MODE_NONE && !m_DoIgnorePixelValue ) { AccessFixedDimensionByItk_2( m_InternalImage, InternalCalculateStatisticsUnmasked, 2, statisticsContainer, histogramContainer ); } else { AccessFixedDimensionByItk_3( m_InternalImage, InternalCalculateStatisticsMasked, 2, m_InternalImageMask2D.GetPointer(), statisticsContainer, histogramContainer ); } } else { MITK_ERROR << "ImageStatistics: Image dimension not supported!"; } // Release unused image smart pointers to free memory m_InternalImage = mitk::Image::ConstPointer(); m_InternalImageMask3D = MaskImage3DType::Pointer(); m_InternalImageMask2D = MaskImage2DType::Pointer(); return true; } const ImageStatisticsCalculator::HistogramType * ImageStatisticsCalculator::GetHistogram( unsigned int timeStep, unsigned int label ) const { if ( m_Image.IsNull() || (timeStep >= m_Image->GetTimeSteps()) ) { return NULL; } switch ( m_MaskingMode ) { case MASKING_MODE_NONE: default: { if(m_DoIgnorePixelValue) return m_MaskedImageHistogramVector[timeStep][label]; return m_ImageHistogramVector[timeStep][label]; } case MASKING_MODE_IMAGE: return m_MaskedImageHistogramVector[timeStep][label]; case MASKING_MODE_PLANARFIGURE: return m_PlanarFigureHistogramVector[timeStep][label]; } } const ImageStatisticsCalculator::HistogramContainer & ImageStatisticsCalculator::GetHistogramVector( unsigned int timeStep ) const { if ( m_Image.IsNull() || (timeStep >= m_Image->GetTimeSteps()) ) { return m_EmptyHistogramContainer; } switch ( m_MaskingMode ) { case MASKING_MODE_NONE: default: { if(m_DoIgnorePixelValue) return m_MaskedImageHistogramVector[timeStep]; return m_ImageHistogramVector[timeStep]; } case MASKING_MODE_IMAGE: return m_MaskedImageHistogramVector[timeStep]; case MASKING_MODE_PLANARFIGURE: return m_PlanarFigureHistogramVector[timeStep]; } } const ImageStatisticsCalculator::Statistics & ImageStatisticsCalculator::GetStatistics( unsigned int timeStep, unsigned int label ) const { if ( m_Image.IsNull() || (timeStep >= m_Image->GetTimeSteps()) ) { return m_EmptyStatistics; } switch ( m_MaskingMode ) { case MASKING_MODE_NONE: default: { if(m_DoIgnorePixelValue) return m_MaskedImageStatisticsVector[timeStep][label]; return m_ImageStatisticsVector[timeStep][label]; } case MASKING_MODE_IMAGE: return m_MaskedImageStatisticsVector[timeStep][label]; case MASKING_MODE_PLANARFIGURE: return m_PlanarFigureStatisticsVector[timeStep][label]; } } const ImageStatisticsCalculator::StatisticsContainer & ImageStatisticsCalculator::GetStatisticsVector( unsigned int timeStep ) const { if ( m_Image.IsNull() || (timeStep >= m_Image->GetTimeSteps()) ) { return m_EmptyStatisticsContainer; } switch ( m_MaskingMode ) { case MASKING_MODE_NONE: default: { if(m_DoIgnorePixelValue) return m_MaskedImageStatisticsVector[timeStep]; return m_ImageStatisticsVector[timeStep]; } case MASKING_MODE_IMAGE: return m_MaskedImageStatisticsVector[timeStep]; case MASKING_MODE_PLANARFIGURE: return m_PlanarFigureStatisticsVector[timeStep]; } } void ImageStatisticsCalculator::ExtractImageAndMask( unsigned int timeStep ) { if ( m_Image.IsNull() ) { throw std::runtime_error( "Error: image empty!" ); } if ( timeStep >= m_Image->GetTimeSteps() ) { throw std::runtime_error( "Error: invalid time step!" ); } ImageTimeSelector::Pointer imageTimeSelector = ImageTimeSelector::New(); imageTimeSelector->SetInput( m_Image ); imageTimeSelector->SetTimeNr( timeStep ); imageTimeSelector->UpdateLargestPossibleRegion(); mitk::Image *timeSliceImage = imageTimeSelector->GetOutput(); switch ( m_MaskingMode ) { case MASKING_MODE_NONE: { m_InternalImage = timeSliceImage; m_InternalImageMask2D = NULL; m_InternalImageMask3D = NULL; if(m_DoIgnorePixelValue) { if( m_InternalImage->GetDimension() == 3 ) { CastToItkImage( timeSliceImage, m_InternalImageMask3D ); m_InternalImageMask3D->FillBuffer(1); } if( m_InternalImage->GetDimension() == 2 ) { CastToItkImage( timeSliceImage, m_InternalImageMask2D ); m_InternalImageMask2D->FillBuffer(1); } } break; } case MASKING_MODE_IMAGE: { if ( m_ImageMask.IsNotNull() && (m_ImageMask->GetReferenceCount() > 1) ) { if ( timeStep < m_ImageMask->GetTimeSteps() ) { ImageTimeSelector::Pointer maskedImageTimeSelector = ImageTimeSelector::New(); maskedImageTimeSelector->SetInput( m_ImageMask ); maskedImageTimeSelector->SetTimeNr( timeStep ); maskedImageTimeSelector->UpdateLargestPossibleRegion(); mitk::Image *timeSliceMaskedImage = maskedImageTimeSelector->GetOutput(); m_InternalImage = timeSliceImage; CastToItkImage( timeSliceMaskedImage, m_InternalImageMask3D ); } else { throw std::runtime_error( "Error: image mask has not enough time steps!" ); } } else { throw std::runtime_error( "Error: image mask empty!" ); } break; } case MASKING_MODE_PLANARFIGURE: { m_InternalImageMask2D = NULL; if ( m_PlanarFigure.IsNull() ) { throw std::runtime_error( "Error: planar figure empty!" ); } if ( !m_PlanarFigure->IsClosed() ) { throw std::runtime_error( "Masking not possible for non-closed figures" ); } const Geometry3D *imageGeometry = timeSliceImage->GetGeometry(); if ( imageGeometry == NULL ) { throw std::runtime_error( "Image geometry invalid!" ); } const Geometry2D *planarFigureGeometry2D = m_PlanarFigure->GetGeometry2D(); if ( planarFigureGeometry2D == NULL ) { throw std::runtime_error( "Planar-Figure not yet initialized!" ); } const PlaneGeometry *planarFigureGeometry = dynamic_cast< const PlaneGeometry * >( planarFigureGeometry2D ); if ( planarFigureGeometry == NULL ) { throw std::runtime_error( "Non-planar planar figures not supported!" ); } // Find principal direction of PlanarFigure in input image unsigned int axis; if ( !this->GetPrincipalAxis( imageGeometry, planarFigureGeometry->GetNormal(), axis ) ) { throw std::runtime_error( "Non-aligned planar figures not supported!" ); } m_PlanarFigureAxis = axis; // Find slice number corresponding to PlanarFigure in input image MaskImage3DType::IndexType index; imageGeometry->WorldToIndex( planarFigureGeometry->GetOrigin(), index ); unsigned int slice = index[axis]; m_PlanarFigureSlice = slice; // Extract slice with given position and direction from image unsigned int dimension = timeSliceImage->GetDimension(); if (dimension != 2) { ExtractImageFilter::Pointer imageExtractor = ExtractImageFilter::New(); imageExtractor->SetInput( timeSliceImage ); imageExtractor->SetSliceDimension( axis ); imageExtractor->SetSliceIndex( slice ); imageExtractor->Update(); m_InternalImage = imageExtractor->GetOutput(); } else { m_InternalImage = timeSliceImage; } // Compute mask from PlanarFigure AccessFixedDimensionByItk_1( m_InternalImage, InternalCalculateMaskFromPlanarFigure, 2, axis ); } } if(m_DoIgnorePixelValue) { if ( m_InternalImage->GetDimension() == 3 ) { AccessFixedDimensionByItk_1( m_InternalImage, InternalMaskIgnoredPixels, 3, m_InternalImageMask3D.GetPointer() ); } else if ( m_InternalImage->GetDimension() == 2 ) { AccessFixedDimensionByItk_1( m_InternalImage, InternalMaskIgnoredPixels, 2, m_InternalImageMask2D.GetPointer() ); } } MITK_DEBUG << "Update of convolution image required?\n m_CalculateHotspot: " << m_CalculateHotspot << "\n m_HotspotSearchConvolutionImage: " << (void*) m_HotspotSearchConvolutionImage.GetPointer() << "\n m_ImageStatisticsCalculationTriggerVector["<GetMTime() << "\n ImageStatistics::MTime: " << this->GetMTime() << "\n m_Image->GetMTime(): " << m_Image->GetMTime(); if( m_CalculateHotspot && ( m_HotspotSearchConvolutionImage.IsNull() || m_Image->GetMTime() > this->GetMTime() // TODO check when m_InternalImage 'really' changes; depends on timeStep || m_HotspotRadiusInMMChanged == true ) ) { MITK_DEBUG <<" --> Update required."; if ( m_InternalImage->GetDimension() == 3 ) { AccessFixedDimensionByItk( m_InternalImage, InternalUpdateConvolutionImage, 3 ); } else if ( m_InternalImage->GetDimension() == 2 ) { AccessFixedDimensionByItk( m_InternalImage, InternalUpdateConvolutionImage, 2 ); } } else { MITK_DEBUG <<" --> Update required."; } } bool ImageStatisticsCalculator::GetPrincipalAxis( const Geometry3D *geometry, Vector3D vector, unsigned int &axis ) { vector.Normalize(); for ( unsigned int i = 0; i < 3; ++i ) { Vector3D axisVector = geometry->GetAxisVector( i ); axisVector.Normalize(); if ( fabs( fabs( axisVector * vector ) - 1.0) < mitk::eps ) { axis = i; return true; } } return false; } template < typename TPixel, unsigned int VImageDimension > void ImageStatisticsCalculator::InternalCalculateStatisticsUnmasked( const itk::Image< TPixel, VImageDimension > *image, StatisticsContainer *statisticsContainer, HistogramContainer* histogramContainer ) { typedef itk::Image< TPixel, VImageDimension > ImageType; typedef itk::Image< unsigned short, VImageDimension > MaskImageType; typedef typename ImageType::IndexType IndexType; typedef itk::Statistics::ScalarImageToHistogramGenerator< ImageType > HistogramGeneratorType; statisticsContainer->clear(); histogramContainer->clear(); // Progress listening... typedef itk::SimpleMemberCommand< ImageStatisticsCalculator > ITKCommandType; ITKCommandType::Pointer progressListener; progressListener = ITKCommandType::New(); progressListener->SetCallbackFunction( this, &ImageStatisticsCalculator::UnmaskedStatisticsProgressUpdate ); // Issue 100 artificial progress events since ScalarIMageToHistogramGenerator // does not (yet?) support progress reporting this->InvokeEvent( itk::StartEvent() ); for ( unsigned int i = 0; i < 100; ++i ) { this->UnmaskedStatisticsProgressUpdate(); } // Calculate statistics (separate filter) typedef itk::StatisticsImageFilter< ImageType > StatisticsFilterType; typename StatisticsFilterType::Pointer statisticsFilter = StatisticsFilterType::New(); statisticsFilter->SetInput( image ); unsigned long observerTag = statisticsFilter->AddObserver( itk::ProgressEvent(), progressListener ); statisticsFilter->Update(); statisticsFilter->RemoveObserver( observerTag ); this->InvokeEvent( itk::EndEvent() ); // Calculate minimum and maximum typedef itk::MinimumMaximumImageCalculator< ImageType > MinMaxFilterType; typename MinMaxFilterType::Pointer minMaxFilter = MinMaxFilterType::New(); minMaxFilter->SetImage( image ); unsigned long observerTag2 = minMaxFilter->AddObserver( itk::ProgressEvent(), progressListener ); minMaxFilter->Compute(); minMaxFilter->RemoveObserver( observerTag2 ); this->InvokeEvent( itk::EndEvent() ); Statistics statistics; //statistics.Reset(); statistics.SetLabel(1); statistics.SetN(image->GetBufferedRegion().GetNumberOfPixels()); statistics.SetMin(statisticsFilter->GetMinimum()); statistics.SetMax(statisticsFilter->GetMaximum()); statistics.SetMean(statisticsFilter->GetMean()); statistics.SetMedian(0.0); statistics.SetSigma(statisticsFilter->GetSigma()); statistics.SetRMS(sqrt( statistics.GetMean() * statistics.GetMean() + statistics.GetSigma() * statistics.GetSigma() )); statistics.GetMinIndex().set_size(image->GetImageDimension()); statistics.GetMaxIndex().set_size(image->GetImageDimension()); vnl_vector tmpMaxIndex; vnl_vector tmpMinIndex; for (unsigned int i=0; iGetIndexOfMaximum()[i]; tmpMinIndex[i] = minMaxFilter->GetIndexOfMinimum()[i]; } statistics.SetMinIndex(tmpMaxIndex); statistics.SetMinIndex(tmpMinIndex); if( IsHotspotCalculated() ) { typedef itk::Image< unsigned short, VImageDimension > MaskImageType; typename MaskImageType::Pointer maskImageITK = MaskImageType::New(); maskImageITK->SetRegions ( image->GetLargestPossibleRegion() ); maskImageITK->Allocate(); typedef itk::ImageRegionIteratorWithIndex< MaskImageType > MaskImageIteratorType; MaskImageIteratorType maskIt(maskImageITK, image->GetLargestPossibleRegion()); for(maskIt.GoToBegin();!maskIt.IsAtEnd(); ++maskIt) maskIt.Set(1); Image::Pointer maskImageMITK = ImportItkImage( maskImageITK ); ImageStatisticsCalculator::Pointer hotspotCalculator = ImageStatisticsCalculator::New(); hotspotCalculator->SetImage( this->m_Image ); hotspotCalculator->SetMaskingModeToImage(); hotspotCalculator->SetImageMask( maskImageMITK ); hotspotCalculator->SetCalculateHotspot( true ); hotspotCalculator->SetHotspotRadiusInMM( GetHotspotRadiusInMM() ); hotspotCalculator->ComputeStatistics(0); // TODO: timestep Statistics hotspotStatistics = hotspotCalculator->GetStatistics(); ImageExtrema hotspotExtrema = CalculateExtremaWorld(image, maskImageITK.GetPointer()); statistics.SetHotspotN(hotspotStatistics.GetHotspotN()); statistics.SetHotspotMax (hotspotStatistics.GetHotspotMax()); statistics.SetHotspotMin(hotspotStatistics.GetHotspotMin()); statistics.SetHotspotMean(hotspotStatistics.GetHotspotMean()); statistics.SetHotspotMedian(0.0); statistics.SetHotspotSigma (hotspotStatistics.GetHotspotSigma()); statistics.SetHotspotRMS (sqrt (statistics.GetHotspotMean() * statistics.GetHotspotMean() + statistics.GetHotspotSigma() * statistics.GetHotspotSigma() )); statistics.SetHotspotMaxIndex(hotspotStatistics.GetHotspotMaxIndex()); statistics.SetHotspotMinIndex(hotspotStatistics.GetHotspotMinIndex()); statistics.SetHotspotIndex(hotspotExtrema.MaxIndex); } statisticsContainer->push_back( statistics ); // Calculate histogram typename HistogramGeneratorType::Pointer histogramGenerator = HistogramGeneratorType::New(); histogramGenerator->SetInput( image ); histogramGenerator->SetMarginalScale( 100 ); histogramGenerator->SetNumberOfBins( 768 ); histogramGenerator->SetHistogramMin( statistics.GetMin() ); histogramGenerator->SetHistogramMax( statistics.GetMax() ); histogramGenerator->Compute(); // TODO DM: add hotspot search here! histogramContainer->push_back( histogramGenerator->GetOutput() ); } template < typename TPixel, unsigned int VImageDimension > void ImageStatisticsCalculator::InternalMaskIgnoredPixels( const itk::Image< TPixel, VImageDimension > *image, itk::Image< unsigned short, VImageDimension > *maskImage ) { typedef itk::Image< TPixel, VImageDimension > ImageType; typedef itk::Image< unsigned short, VImageDimension > MaskImageType; itk::ImageRegionIterator itmask(maskImage, maskImage->GetLargestPossibleRegion()); itk::ImageRegionConstIterator itimage(image, image->GetLargestPossibleRegion()); itmask.GoToBegin(); itimage.GoToBegin(); while( !itmask.IsAtEnd() ) { if(m_IgnorePixelValue == itimage.Get()) { itmask.Set(0); } ++itmask; ++itimage; } } template < typename TPixel, unsigned int VImageDimension > void ImageStatisticsCalculator::InternalCalculateStatisticsMasked( const itk::Image< TPixel, VImageDimension > *image, itk::Image< unsigned short, VImageDimension > *maskImage, StatisticsContainer* statisticsContainer, HistogramContainer* histogramContainer ) { typedef itk::Image< TPixel, VImageDimension > ImageType; typedef itk::Image< unsigned short, VImageDimension > MaskImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::PointType PointType; typedef typename ImageType::SpacingType SpacingType; typedef itk::LabelStatisticsImageFilter< ImageType, MaskImageType > LabelStatisticsFilterType; typedef itk::ChangeInformationImageFilter< MaskImageType > ChangeInformationFilterType; typedef itk::ExtractImageFilter< ImageType, ImageType > ExtractImageFilterType; statisticsContainer->clear(); histogramContainer->clear(); // Make sure that mask is set if ( maskImage == NULL ) { itkExceptionMacro( << "Mask image needs to be set!" ); } // Make sure that spacing of mask and image are the same SpacingType imageSpacing = image->GetSpacing(); SpacingType maskSpacing = maskImage->GetSpacing(); PointType zeroPoint; zeroPoint.Fill( 0.0 ); if ( (zeroPoint + imageSpacing).SquaredEuclideanDistanceTo( (zeroPoint + maskSpacing) ) > mitk::eps ) { itkExceptionMacro( << "Mask needs to have same spacing as image! (Image spacing: " << imageSpacing << "; Mask spacing: " << maskSpacing << ")" ); } // Make sure that orientation of mask and image are the same typedef typename ImageType::DirectionType DirectionType; DirectionType imageDirection = image->GetDirection(); DirectionType maskDirection = maskImage->GetDirection(); for( int i = 0; i < imageDirection.ColumnDimensions; ++i ) { for( int j = 0; j < imageDirection.ColumnDimensions; ++j ) { double differenceDirection = imageDirection[i][j] - maskDirection[i][j]; if ( fabs( differenceDirection ) > mitk::eps ) { itkExceptionMacro( << "Mask needs to have same direction as image! (Image direction: " << imageDirection << "; Mask direction: " << maskDirection << ")" ); } } } // Make sure that the voxels of mask and image are correctly "aligned", i.e., voxel boundaries are the same in both images PointType imageOrigin = image->GetOrigin(); PointType maskOrigin = maskImage->GetOrigin(); long offset[ImageType::ImageDimension]; typedef itk::ContinuousIndex ContinousIndexType; ContinousIndexType maskOriginContinousIndex, imageOriginContinousIndex; image->TransformPhysicalPointToContinuousIndex(maskOrigin, maskOriginContinousIndex); image->TransformPhysicalPointToContinuousIndex(imageOrigin, imageOriginContinousIndex); for ( unsigned int i = 0; i < ImageType::ImageDimension; ++i ) { double misalignment = maskOriginContinousIndex[i] - floor( maskOriginContinousIndex[i] + 0.5 ); if ( fabs( misalignment ) > mitk::eps ) { itkExceptionMacro( << "Pixels/voxels of mask and image are not sufficiently aligned! (Misalignment: " << misalignment << ")" ); } double indexCoordDistance = maskOriginContinousIndex[i] - imageOriginContinousIndex[i]; offset[i] = (int) indexCoordDistance + image->GetBufferedRegion().GetIndex()[i]; } // Adapt the origin and region (index/size) of the mask so that the origin of both are the same typename ChangeInformationFilterType::Pointer adaptMaskFilter; adaptMaskFilter = ChangeInformationFilterType::New(); adaptMaskFilter->ChangeOriginOn(); adaptMaskFilter->ChangeRegionOn(); adaptMaskFilter->SetInput( maskImage ); adaptMaskFilter->SetOutputOrigin( image->GetOrigin() ); adaptMaskFilter->SetOutputOffset( offset ); adaptMaskFilter->Update(); typename MaskImageType::Pointer adaptedMaskImage = adaptMaskFilter->GetOutput(); // Make sure that mask region is contained within image region if ( !image->GetLargestPossibleRegion().IsInside( adaptedMaskImage->GetLargestPossibleRegion() ) ) { itkExceptionMacro( << "Mask region needs to be inside of image region! (Image region: " << image->GetLargestPossibleRegion() << "; Mask region: " << adaptedMaskImage->GetLargestPossibleRegion() << ")" ); } // If mask region is smaller than image region, extract the sub-sampled region from the original image typename ImageType::SizeType imageSize = image->GetBufferedRegion().GetSize(); typename ImageType::SizeType maskSize = maskImage->GetBufferedRegion().GetSize(); bool maskSmallerImage = false; for ( unsigned int i = 0; i < ImageType::ImageDimension; ++i ) { if ( maskSize[i] < imageSize[i] ) { maskSmallerImage = true; } } typename ImageType::ConstPointer adaptedImage; if ( maskSmallerImage ) { typename ExtractImageFilterType::Pointer extractImageFilter = ExtractImageFilterType::New(); extractImageFilter->SetInput( image ); extractImageFilter->SetExtractionRegion( adaptedMaskImage->GetBufferedRegion() ); extractImageFilter->Update(); adaptedImage = extractImageFilter->GetOutput(); } else { adaptedImage = image; } // Initialize Filter typedef itk::StatisticsImageFilter< ImageType > StatisticsFilterType; typename StatisticsFilterType::Pointer statisticsFilter = StatisticsFilterType::New(); statisticsFilter->SetInput( adaptedImage ); statisticsFilter->Update(); int numberOfBins = ( m_DoIgnorePixelValue && (m_MaskingMode == MASKING_MODE_NONE) ) ? 768 : 384; typename LabelStatisticsFilterType::Pointer labelStatisticsFilter; labelStatisticsFilter = LabelStatisticsFilterType::New(); labelStatisticsFilter->SetInput( adaptedImage ); labelStatisticsFilter->SetLabelInput( adaptedMaskImage ); labelStatisticsFilter->UseHistogramsOn(); labelStatisticsFilter->SetHistogramParameters( numberOfBins, statisticsFilter->GetMinimum(), statisticsFilter->GetMaximum() ); // Add progress listening typedef itk::SimpleMemberCommand< ImageStatisticsCalculator > ITKCommandType; ITKCommandType::Pointer progressListener; progressListener = ITKCommandType::New(); progressListener->SetCallbackFunction( this, &ImageStatisticsCalculator::MaskedStatisticsProgressUpdate ); unsigned long observerTag = labelStatisticsFilter->AddObserver( itk::ProgressEvent(), progressListener ); // Execute filter this->InvokeEvent( itk::StartEvent() ); // Make sure that only the mask region is considered (otherwise, if the mask region is smaller // than the image region, the Update() would result in an exception). labelStatisticsFilter->GetOutput()->SetRequestedRegion( adaptedMaskImage->GetLargestPossibleRegion() ); // Execute the filter labelStatisticsFilter->Update(); this->InvokeEvent( itk::EndEvent() ); labelStatisticsFilter->RemoveObserver( observerTag ); // Find all relevant labels of mask (other than 0) std::list< int > relevantLabels; bool maskNonEmpty = false; unsigned int i; for ( i = 1; i < 4096; ++i ) { if ( labelStatisticsFilter->HasLabel( i ) ) { relevantLabels.push_back( i ); maskNonEmpty = true; } } MITK_INFO <<"Mask has pixels? " << maskNonEmpty; if ( maskNonEmpty ) { - Statistics statistics; + Statistics statistics; // restore previous code std::list< int >::iterator it; for ( it = relevantLabels.begin(), i = 0; it != relevantLabels.end(); ++it, ++i ) { histogramContainer->push_back( HistogramType::ConstPointer( labelStatisticsFilter->GetHistogram( (*it) ) ) ); statistics.SetLabel (*it); statistics.SetN(labelStatisticsFilter->GetCount( *it )); statistics.SetMin(labelStatisticsFilter->GetMinimum( *it )); statistics.SetMax(labelStatisticsFilter->GetMaximum( *it )); statistics.SetMean(labelStatisticsFilter->GetMean( *it )); statistics.SetMedian(labelStatisticsFilter->GetMedian( *it )); statistics.SetSigma(labelStatisticsFilter->GetSigma( *it )); statistics.SetRMS(sqrt( statistics.GetMean() * statistics.GetMean() + statistics.GetSigma() * statistics.GetSigma() )); // restrict image to mask area for min/max index calculation typedef itk::MaskImageFilter< ImageType, MaskImageType, ImageType > MaskImageFilterType; typename MaskImageFilterType::Pointer masker = MaskImageFilterType::New(); masker->SetOutsideValue( (statistics.GetMin()+statistics.GetMax())/2 ); masker->SetInput1(adaptedImage); masker->SetInput2(adaptedMaskImage); masker->Update(); // get index of minimum and maximum typedef itk::MinimumMaximumImageCalculator< ImageType > MinMaxFilterType; typename MinMaxFilterType::Pointer minMaxFilter = MinMaxFilterType::New(); minMaxFilter->SetImage( masker->GetOutput() ); unsigned long observerTag2 = minMaxFilter->AddObserver( itk::ProgressEvent(), progressListener ); minMaxFilter->Compute(); minMaxFilter->RemoveObserver( observerTag2 ); this->InvokeEvent( itk::EndEvent() ); typename MinMaxFilterType::IndexType tempMaxIndex = minMaxFilter->GetIndexOfMaximum(); typename MinMaxFilterType::IndexType tempMinIndex = minMaxFilter->GetIndexOfMinimum(); // FIX BUG 14644 //If a PlanarFigure is used for segmentation the //adaptedImage is a single slice (2D). Adding the // 3. dimension. vnl_vector maxIndex; vnl_vector minIndex; maxIndex.set_size(m_Image->GetDimension()); minIndex.set_size(m_Image->GetDimension()); if (m_MaskingMode == MASKING_MODE_PLANARFIGURE && m_Image->GetDimension()==3) { maxIndex[m_PlanarFigureCoordinate0] = tempMaxIndex[0]; maxIndex[m_PlanarFigureCoordinate1] = tempMaxIndex[1]; maxIndex[m_PlanarFigureAxis] = m_PlanarFigureSlice; minIndex[m_PlanarFigureCoordinate0] = tempMinIndex[0] ; minIndex[m_PlanarFigureCoordinate1] = tempMinIndex[1]; minIndex[m_PlanarFigureAxis] = m_PlanarFigureSlice; } else { for (unsigned int i = 0; ipush_back( statistics ); } else { histogramContainer->push_back( HistogramType::ConstPointer( m_EmptyHistogram ) ); statisticsContainer->push_back( Statistics() ); // TODO DM: this is uninitialized! (refactor into real class!) } } // TODO DM: needs to be modified to calculate a specific or multiple(!) labels template ImageStatisticsCalculator::ImageExtrema ImageStatisticsCalculator::CalculateExtremaWorld( const itk::Image *inputImage, itk::Image *maskImage) { // TODO iterate onlye the region of the mask (in the inputImage), which is usually smaller (not sure if this is possible) typedef itk::Image< TPixel, VImageDimension > ImageType; typedef itk::Image< unsigned short, VImageDimension > MaskImageType; typedef itk::ImageRegionConstIteratorWithIndex MaskImageIteratorType; typedef itk::ImageRegionConstIteratorWithIndex InputImageIndexIteratorType; MaskImageIteratorType maskIt(maskImage, inputImage->GetLargestPossibleRegion()); InputImageIndexIteratorType imageIndexIt(inputImage, inputImage->GetLargestPossibleRegion()); float maxValue = itk::NumericTraits::min(); float minValue = itk::NumericTraits::max(); typename ImageType::IndexType maxIndex; typename ImageType::IndexType minIndex; typename ImageType::IndexType imageIndex; typename ImageType::PointType worldPosition; typename ImageType::IndexType maskIndex; for(imageIndexIt.GoToBegin(); !imageIndexIt.IsAtEnd(); ++imageIndexIt) { imageIndex = imageIndexIt.GetIndex(); inputImage->TransformIndexToPhysicalPoint(imageIndex, worldPosition); maskImage->TransformPhysicalPointToIndex(worldPosition, maskIndex); maskIt.SetIndex( maskIndex ); if(maskIt.Get() > 0) { double value = imageIndexIt.Get(); //Calculate minimum, maximum and corresponding index-values if( value > maxValue ) { maxIndex = imageIndexIt.GetIndex(); maxValue = value; } if(value < minValue ) { minIndex = imageIndexIt.GetIndex(); minValue = value; } } } ImageExtrema minMax; minMax.MinIndex.set_size(inputImage->GetImageDimension()); minMax.MaxIndex.set_size(inputImage->GetImageDimension()); for(unsigned int i = 0; i < minMax.MaxIndex.size(); ++i) minMax.MaxIndex[i] = maxIndex[i]; for(unsigned int i = 0; i < minMax.MinIndex.size(); ++i) minMax.MinIndex[i] = minIndex[i]; minMax.Max = maxValue; minMax.Min = minValue; return minMax; } template itk::Size ImageStatisticsCalculator ::CalculateConvolutionKernelSize(double spacing[VImageDimension], double radiusInMM) { typedef itk::Image< float, VImageDimension > KernelImageType; typedef typename KernelImageType::SizeType SizeType; SizeType maskSize; for(unsigned int i = 0; i < VImageDimension; ++i) { maskSize[i] = ::ceil( 2.0 * radiusInMM / spacing[i] ); // We always want an uneven size to have a clear center point in the convolution mask if(maskSize[i] % 2 == 0 ) { ++maskSize[i]; } } return maskSize; } template itk::SmartPointer< itk::Image > ImageStatisticsCalculator ::GenerateHotspotSearchConvolutionKernel(double spacing[VImageDimension], double radiusInMM) { std::stringstream ss; for (unsigned int i = 0; i < VImageDimension; ++i) { ss << spacing[i]; if (i < VImageDimension -1) ss << ","; } MITK_DEBUG << "Update convolution kernel for spacing (" << ss.str() << ") and radius " << radiusInMM << "mm"; double radiusInMMSquared = radiusInMM * radiusInMM; typedef itk::Image< float, VImageDimension > KernelImageType; typename KernelImageType::Pointer convolutionKernel = KernelImageType::New(); // Calculate size and allocate mask image typedef typename KernelImageType::IndexType IndexType; IndexType maskIndex; maskIndex.Fill(0); typedef typename KernelImageType::SizeType SizeType; SizeType maskSize = this->CalculateConvolutionKernelSize(spacing, radiusInMM); Point3D convolutionMaskCenter; convolutionMaskCenter.Fill(0.0); for(unsigned int i = 0; i < VImageDimension; ++i) { convolutionMaskCenter[i] = 0.5 * (double)(maskSize[i]-1); } typedef typename KernelImageType::RegionType RegionType; RegionType maskRegion; maskRegion.SetSize(maskSize); maskRegion.SetIndex(maskIndex); convolutionKernel->SetRegions(maskRegion); convolutionKernel->SetSpacing(spacing); convolutionKernel->Allocate(); // Fill mask image values by subsampling the image grid typedef itk::ImageRegionIteratorWithIndex MaskIteratorType; MaskIteratorType maskIt(convolutionKernel,maskRegion); int numberOfSubVoxelsPerDimension = 2; // per dimension! int numberOfSubVoxels = ::pow( static_cast(numberOfSubVoxelsPerDimension), static_cast(VImageDimension) ); double subVoxelSize = 1.0 / (double)numberOfSubVoxelsPerDimension; double valueOfOneSubVoxel = 1.0 / (double)numberOfSubVoxels; double maskValue = 0.0; Point3D subVoxelPosition; double distanceSquared = 0.0; typedef itk::ContinuousIndex ContinuousIndexType; for(maskIt.GoToBegin(); !maskIt.IsAtEnd(); ++maskIt) { ContinuousIndexType indexPoint(maskIt.GetIndex()); Point3D voxelPosition; for (unsigned int dimension = 0; dimension < VImageDimension; ++dimension) { voxelPosition[dimension] = indexPoint[dimension]; } // TODO DM: regard all dimensions, including z! (former code used only x/y) // TODO DM: generalize: not x, y, z but a for loop over dimension // TODO DM: this could be done by calling a recursive method, handing over the "remaining number of dimensions to iterate" maskValue = 0.0; Vector3D subVoxelOffset; subVoxelOffset.Fill(0.0); // iterate sub-voxels by iterating all possible offsets for (subVoxelOffset[0] = -0.5 + subVoxelSize / 2.0; subVoxelOffset[0] < +0.5; subVoxelOffset[0] += subVoxelSize) { for (subVoxelOffset[1] = -0.5 + subVoxelSize / 2.0; subVoxelOffset[1] < +0.5; subVoxelOffset[1] += subVoxelSize) { for (subVoxelOffset[2] = -0.5 + subVoxelSize / 2.0; subVoxelOffset[2] < +0.5; subVoxelOffset[2] += subVoxelSize) { subVoxelPosition = voxelPosition + subVoxelOffset; // TODO DM: this COULD be integrated into the for-loops if neccessary (add voxelPosition to initializer and end condition) //if ( subVoxelPosition.EuclideanDistanceTo( convolutionMaskCenter ) < radiusInMM ) // TODO DM: this is too much matrix operations, we calculate ourselves, check if this time is relevant // maskValue += valueOfOneSubVoxel; distanceSquared = (subVoxelPosition[0]-convolutionMaskCenter[0]) / spacing[0] * (subVoxelPosition[0]-convolutionMaskCenter[0]) / spacing[0] + (subVoxelPosition[1]-convolutionMaskCenter[1]) / spacing[1] * (subVoxelPosition[1]-convolutionMaskCenter[1]) / spacing[1] + (subVoxelPosition[2]-convolutionMaskCenter[2]) / spacing[2] * (subVoxelPosition[2]-convolutionMaskCenter[2]) / spacing[2]; if (distanceSquared <= radiusInMMSquared) { maskValue += valueOfOneSubVoxel; } } } } maskIt.Set( maskValue ); } return convolutionKernel; } template void ImageStatisticsCalculator::InternalUpdateConvolutionImage( itk::Image* inputImage ) { double spacing[VImageDimension]; for (unsigned int dimension = 0; dimension < VImageDimension; ++dimension) { spacing[dimension] = inputImage->GetSpacing()[dimension]; } // update convolution kernel typedef itk::Image< float, VImageDimension > KernelImageType; typename KernelImageType::Pointer convolutionKernel = this->GenerateHotspotSearchConvolutionKernel(spacing, m_HotspotRadiusInMM); // TODO: if GenerateHotspotSearchConvolutionKernel() consumes relevant time, we need an additional condition // update convolution image typedef itk::Image< TPixel, VImageDimension > InputImageType; typedef itk::Image< TPixel, VImageDimension > ConvolutionImageType; typedef itk::FFTConvolutionImageFilter ConvolutionFilterType; typedef itk::ConstantBoundaryCondition BoundaryConditionType; BoundaryConditionType boundaryCondition; boundaryCondition.SetConstant(0.0); typename ConvolutionFilterType::Pointer convolutionFilter = ConvolutionFilterType::New(); convolutionFilter->SetBoundaryCondition(&boundaryCondition); convolutionFilter->SetInput(inputImage); convolutionFilter->SetKernelImage(convolutionKernel); convolutionFilter->SetNormalize(true); //MITK_INFO << "Update Convolution image for hotspot search"; convolutionFilter->UpdateLargestPossibleRegion(); // TODO check if we could benefit from restricting this for a region typename ConvolutionImageType::Pointer convolutionImage = convolutionFilter->GetOutput(); convolutionImage->SetSpacing( inputImage->GetSpacing() ); // TODO: only workaround because convolution filter seems to ignore spacing of input image m_HotspotSearchConvolutionImage = convolutionImage.GetPointer(); m_HotspotRadiusInMMChanged = false; } template < typename TPixel, unsigned int VImageDimension> void ImageStatisticsCalculator ::FillHotspotMaskPixels( itk::Image* maskImage, itk::Point sphereCenter, double sphereRadiusInMM) { typedef itk::Image< TPixel, VImageDimension > MaskImageType; typedef itk::ImageRegionIteratorWithIndex MaskImageIteratorType; MaskImageIteratorType maskIt(maskImage, maskImage->GetLargestPossibleRegion()); // TODO DM: we should use the same regions here typename MaskImageType::IndexType maskIndex; typename MaskImageType::PointType worldPosition; for(maskIt.GoToBegin(); !maskIt.IsAtEnd(); ++maskIt) { maskIndex = maskIt.GetIndex(); maskImage->TransformIndexToPhysicalPoint(maskIndex, worldPosition); maskIt.Set( worldPosition.EuclideanDistanceTo(sphereCenter) <= sphereRadiusInMM ? 1 : 0 ); } } template < typename TPixel, unsigned int VImageDimension> ImageStatisticsCalculator::Statistics ImageStatisticsCalculator::CalculateHotspotStatistics( const itk::Image* inputImage, itk::Image* maskImage, double radiusInMM) { //MITK_INFO << "CalculateHotspotStatistics()"; // get convolution image (updated in InternalUpdateConvolutionImage()) typedef itk::Image< TPixel, VImageDimension > ConvolutionImageType; typedef itk::Image< float, VImageDimension > KernelImageType; typedef itk::Image< unsigned short, VImageDimension > MaskImageType; typename ConvolutionImageType::Pointer convolutionImage = dynamic_cast(m_HotspotSearchConvolutionImage.GetPointer()); if (convolutionImage.IsNull()) { MITK_ERROR << "Empty convolution image in CalculateHotspotStatistics(). We should never reach this state (logic error)."; throw std::logic_error("Empty convolution image in CalculateHotspotStatistics()"); } // find maximum in convolution image, given the current mask (this might change over time, while we assume the input fixed (TODO wrong assumption)) ImageExtrema pi = CalculateExtremaWorld(convolutionImage.GetPointer(), maskImage); /* MITK_INFO <<"Initial search for hotspot: " "\n Index: " << pi.MaxIndex[0] << "," << pi.MaxIndex[1] << "," << pi.MaxIndex[2] << "\n Value(hotspot): " << pi.Max<< "\n Index(min): " << pi.MinIndex[0] << "," << pi.MinIndex[1] << "," << pi.MinIndex[2] << "\n Value(min): " << pi.Min;*/ // create mask corresponding to hotspot region // mask is defined on the inputImage grid and is // dimensioned as the hotspot convolution kernel (the sphere) double spacing[VImageDimension]; for (unsigned int dimension = 0; dimension < VImageDimension; ++dimension) { spacing[dimension] = inputImage->GetSpacing()[dimension]; } typedef typename ConvolutionImageType::SizeType SizeType; SizeType maskSize = this->CalculateConvolutionKernelSize(spacing, m_HotspotRadiusInMM); typedef typename ConvolutionImageType::IndexType IndexType; IndexType maskIndex; maskIndex.Fill(0); //MITK_INFO << "Hotspot statistics mask started with size ["< shift -2 required if (maskIndex[dimension] < 0) { maskIndex[dimension] = 0; } /* TODO if (maskIndex[dimension] + maskSize[dimension] > inputImage->GetBufferedRegion().GetSize()[dimension] ) { maskSize[dimension] = inputImage->GetBufferedRegionLargestPossibleRegion().GetSize()[dimension] - maskIndex[dimension]; } */ } //MITK_INFO << "Hotspot statistics mask corrected as region of size ["<CopyInformation( inputImage ); // type not optimal, but image grid is good typedef typename ConvolutionImageType::RegionType RegionType; RegionType hotspotMaskRegion; IndexType mi; mi.Fill(0); hotspotMaskRegion.SetIndex( mi ); hotspotMaskRegion.SetSize( maskSize ); hotspotMaskITK->SetRegions( hotspotMaskRegion ); hotspotMaskITK->Allocate(); typename ConvolutionImageType::PointType maskOrigin; inputImage->TransformIndexToPhysicalPoint(maskIndex,maskOrigin); //MITK_INFO << "Mask origin at: " << maskOrigin; hotspotMaskITK->SetOrigin(maskOrigin); IndexType maskCenterIndex; for (unsigned int d =0; d< VImageDimension;++d) maskCenterIndex[d]=pi.MaxIndex[d]; typename ConvolutionImageType::PointType maskCenter; inputImage->TransformIndexToPhysicalPoint(maskCenterIndex,maskCenter); //MITK_INFO << "Mask center in input image: " << maskCenter; - FillHotspotMaskPixels(hotspotMaskITK.GetPointer(), maskCenter, m_HotspotRadiusInMM); + this->FillHotspotMaskPixels(hotspotMaskITK.GetPointer(), maskCenter, m_HotspotRadiusInMM); Image::Pointer hotspotMaskMITK = ImportItkImage( hotspotMaskITK ); Point3D maskCenterPosition = hotspotMaskMITK->GetGeometry()->GetCenter(); //MITK_INFO << "Mask center: " << maskCenterPosition; // use second instance of ImageStatisticsCalculator to calculate hotspot statistics + Image::Pointer hotspotInputMITK = ImportItkImage( inputImage ); + ImageStatisticsCalculator::Pointer calculator = ImageStatisticsCalculator::New(); - calculator->SetImage( this->m_Image ); + calculator->SetImage( hotspotInputMITK ); calculator->SetMaskingModeToImage(); calculator->SetImageMask( hotspotMaskMITK ); calculator->SetCalculateHotspot( false ); - calculator->ComputeStatistics(0); // TODO: timestep + calculator->ComputeStatistics(0); // timestep 0, because inputImage already IS the image of timestep N (from perspective of ImageStatisticsCalculator caller) Statistics hotspotMaskStatistics = calculator->GetStatistics(0); Statistics hotspotStatistics; hotspotStatistics.SetHotspotMin(hotspotMaskStatistics.GetMin()); hotspotStatistics.SetHotspotMinIndex(hotspotMaskStatistics.GetMinIndex()); hotspotStatistics.SetHotspotMax(hotspotMaskStatistics.GetMax()); hotspotStatistics.SetHotspotMaxIndex(hotspotMaskStatistics.GetMaxIndex()); hotspotStatistics.SetHotspotMean(hotspotMaskStatistics.GetMean()); hotspotStatistics.SetHotspotMedian(hotspotMaskStatistics.GetMedian()); hotspotStatistics.SetHotspotIndex(pi.MaxIndex); hotspotStatistics.SetHotspotSigma(hotspotMaskStatistics.GetSigma()); hotspotStatistics.SetHotspotRMS(sqrt( hotspotMaskStatistics.GetMean() * hotspotMaskStatistics.GetMean() + hotspotMaskStatistics.GetSigma() * hotspotMaskStatistics.GetSigma() )); hotspotStatistics.SetHotspotN(hotspotMaskStatistics.GetN()); /*MITK_INFO << "----- Hotspot search results:" "\n Index: " << hotspotStatistics.HotspotIndex[0] << "," << hotspotStatistics.HotspotIndex[1] << "," << hotspotStatistics.HotspotIndex[2] << "\n Value: " << hotspotStatistics.HotspotMean << "\n Max Index: " << hotspotStatistics.HotspotMaxIndex[0] << "," << hotspotStatistics.HotspotMaxIndex[1] << "," << hotspotStatistics.HotspotMaxIndex[2] << "\n Max Value: " << hotspotStatistics.HotspotMax << "\n Min Index: " << hotspotStatistics.HotspotMinIndex[0] << "," << hotspotStatistics.HotspotMinIndex[1] << "," << hotspotStatistics.HotspotMinIndex[2] << "\n Min Value: " << hotspotStatistics.HotspotMin;*/ return hotspotStatistics; } template < typename TPixel, unsigned int VImageDimension > void ImageStatisticsCalculator::InternalCalculateMaskFromPlanarFigure( const itk::Image< TPixel, VImageDimension > *image, unsigned int axis ) { typedef itk::Image< TPixel, VImageDimension > ImageType; typedef itk::CastImageFilter< ImageType, MaskImage2DType > CastFilterType; // Generate mask image as new image with same header as input image and // initialize with 1. typename CastFilterType::Pointer castFilter = CastFilterType::New(); castFilter->SetInput( image ); castFilter->Update(); castFilter->GetOutput()->FillBuffer( 1 ); // all PolylinePoints of the PlanarFigure are stored in a vtkPoints object. // These points are used by the vtkLassoStencilSource to create // a vtkImageStencil. const mitk::Geometry2D *planarFigureGeometry2D = m_PlanarFigure->GetGeometry2D(); const typename PlanarFigure::PolyLineType planarFigurePolyline = m_PlanarFigure->GetPolyLine( 0 ); const mitk::Geometry3D *imageGeometry3D = m_Image->GetGeometry( 0 ); // Determine x- and y-dimensions depending on principal axis int i0, i1; switch ( axis ) { case 0: i0 = 1; i1 = 2; break; case 1: i0 = 0; i1 = 2; break; case 2: default: i0 = 0; i1 = 1; break; } m_PlanarFigureCoordinate0= i0; m_PlanarFigureCoordinate1= i1; // store the polyline contour as vtkPoints object bool outOfBounds = false; vtkSmartPointer points = vtkSmartPointer::New(); typename PlanarFigure::PolyLineType::const_iterator it; for ( it = planarFigurePolyline.begin(); it != planarFigurePolyline.end(); ++it ) { Point3D point3D; // Convert 2D point back to the local index coordinates of the selected // image planarFigureGeometry2D->Map( it->Point, point3D ); // Polygons (partially) outside of the image bounds can not be processed // further due to a bug in vtkPolyDataToImageStencil if ( !imageGeometry3D->IsInside( point3D ) ) { outOfBounds = true; } imageGeometry3D->WorldToIndex( point3D, point3D ); points->InsertNextPoint( point3D[i0], point3D[i1], 0 ); } // mark a malformed 2D planar figure ( i.e. area = 0 ) as out of bounds // this can happen when all control points of a rectangle lie on the same line = two of the three extents are zero double bounds[6] = {0, 0, 0, 0, 0, 0}; points->GetBounds( bounds ); bool extent_x = (fabs(bounds[0] - bounds[1])) < mitk::eps; bool extent_y = (fabs(bounds[2] - bounds[3])) < mitk::eps; bool extent_z = (fabs(bounds[4] - bounds[5])) < mitk::eps; // throw an exception if a closed planar figure is deformed, i.e. has only one non-zero extent if ( m_PlanarFigure->IsClosed() && ((extent_x && extent_y) || (extent_x && extent_z) || (extent_y && extent_z))) { mitkThrow() << "Figure has a zero area and cannot be used for masking."; } if ( outOfBounds ) { throw std::runtime_error( "Figure at least partially outside of image bounds!" ); } // create a vtkLassoStencilSource and set the points of the Polygon vtkSmartPointer lassoStencil = vtkSmartPointer::New(); lassoStencil->SetShapeToPolygon(); lassoStencil->SetPoints( points ); // Export from ITK to VTK (to use a VTK filter) typedef itk::VTKImageImport< MaskImage2DType > ImageImportType; typedef itk::VTKImageExport< MaskImage2DType > ImageExportType; typename ImageExportType::Pointer itkExporter = ImageExportType::New(); itkExporter->SetInput( castFilter->GetOutput() ); vtkSmartPointer vtkImporter = vtkSmartPointer::New(); this->ConnectPipelines( itkExporter, vtkImporter ); // Apply the generated image stencil to the input image vtkSmartPointer imageStencilFilter = vtkSmartPointer::New(); imageStencilFilter->SetInputConnection( vtkImporter->GetOutputPort() ); imageStencilFilter->SetStencil( lassoStencil->GetOutput() ); imageStencilFilter->ReverseStencilOff(); imageStencilFilter->SetBackgroundValue( 0 ); imageStencilFilter->Update(); // Export from VTK back to ITK vtkSmartPointer vtkExporter = vtkImageExport::New(); // TODO: this is WRONG, should be vtkSmartPointer::New(), but bug # 14455 vtkExporter->SetInputConnection( imageStencilFilter->GetOutputPort() ); vtkExporter->Update(); typename ImageImportType::Pointer itkImporter = ImageImportType::New(); this->ConnectPipelines( vtkExporter, itkImporter ); itkImporter->Update(); // Store mask m_InternalImageMask2D = itkImporter->GetOutput(); } void ImageStatisticsCalculator::UnmaskedStatisticsProgressUpdate() { // Need to throw away every second progress event to reach a final count of // 100 since two consecutive filters are used in this case static int updateCounter = 0; if ( updateCounter++ % 2 == 0 ) { this->InvokeEvent( itk::ProgressEvent() ); } } void ImageStatisticsCalculator::MaskedStatisticsProgressUpdate() { this->InvokeEvent( itk::ProgressEvent() ); } // Get-functions statistics unsigned int ImageStatisticsCalculator::Statistics::GetN() const { return m_N; } double ImageStatisticsCalculator::Statistics::GetMean() const { return m_Mean; } double ImageStatisticsCalculator::Statistics::GetMin() const { return m_Min; } double ImageStatisticsCalculator::Statistics::GetMax() const { return m_Max; } double ImageStatisticsCalculator::Statistics::GetMedian() const { return m_Median; } double ImageStatisticsCalculator::Statistics::GetVariance() const { return m_Variance; } double ImageStatisticsCalculator::Statistics::GetSigma() const { return m_Sigma; } double ImageStatisticsCalculator::Statistics::GetRMS() const { return m_RMS; } vnl_vector ImageStatisticsCalculator::Statistics::GetMaxIndex() const { return m_MaxIndex; } vnl_vector ImageStatisticsCalculator::Statistics::GetMinIndex() const { return m_MinIndex; } // Set-fucntions statistics void ImageStatisticsCalculator::Statistics::SetLabel(unsigned int label) { m_Label = label; } void ImageStatisticsCalculator::Statistics::SetN(unsigned int n) { m_N = n; } void ImageStatisticsCalculator::Statistics::SetMean(double mean) { m_Mean = mean; } void ImageStatisticsCalculator::Statistics::SetMin(double min) { m_Min = min; } void ImageStatisticsCalculator::Statistics::SetMax(double max) { m_Max = max; } void ImageStatisticsCalculator::Statistics::SetMedian(double median) { m_Median = median; } void ImageStatisticsCalculator::Statistics::SetVariance(double variance) { m_Variance = variance; } void ImageStatisticsCalculator::Statistics::SetSigma(double sigma) { m_Sigma = sigma; } void ImageStatisticsCalculator::Statistics::SetRMS(double rms) { m_RMS = rms; } void ImageStatisticsCalculator::Statistics::SetMaxIndex(vnl_vector maxIndex) { m_MaxIndex = maxIndex; } void ImageStatisticsCalculator::Statistics::SetMinIndex(vnl_vector minIndex) { m_MinIndex = minIndex; } // Get-fucntions hotspot-statistics unsigned int ImageStatisticsCalculator::Statistics::GetHotspotN() const { return m_HotspotN; } double ImageStatisticsCalculator::Statistics::GetHotspotMean() const { return m_HotspotMean; } double ImageStatisticsCalculator::Statistics::GetHotspotMin() const { return m_HotspotMin; } double ImageStatisticsCalculator::Statistics::GetHotspotMax() const { return m_HotspotMax; } double ImageStatisticsCalculator::Statistics::GetHotspotMedian() const { return m_HotspotMedian; } double ImageStatisticsCalculator::Statistics::GetHotspotVariance() const { return m_HotspotVariance; } double ImageStatisticsCalculator::Statistics::GetHotspotSigma() const { return m_HotspotSigma; } double ImageStatisticsCalculator::Statistics::GetHotspotRMS() const { return m_HotspotRMS; } vnl_vector ImageStatisticsCalculator::Statistics::GetHotspotMaxIndex() const { return m_HotspotMaxIndex; } vnl_vector ImageStatisticsCalculator::Statistics::GetHotspotMinIndex() const { return m_HotspotMinIndex; } vnl_vector ImageStatisticsCalculator::Statistics::GetHotspotIndex() const {return m_HotspotIndex;} // Set-functions hotspot-statistics void ImageStatisticsCalculator::Statistics::SetHotspotN(unsigned int n) { m_HotspotN = n; } void ImageStatisticsCalculator::Statistics::SetHotspotMean(double mean) { m_HotspotMean = mean; } void ImageStatisticsCalculator::Statistics::SetHotspotMin(double min) { m_HotspotMin = min; } void ImageStatisticsCalculator::Statistics::SetHotspotMax(double max) { m_HotspotMax = max; } void ImageStatisticsCalculator::Statistics::SetHotspotMedian(double median) { m_HotspotMedian = median; } void ImageStatisticsCalculator::Statistics::SetHotspotVariance(double variance) { m_HotspotVariance = variance; } void ImageStatisticsCalculator::Statistics::SetHotspotSigma(double sigma) { m_HotspotSigma = sigma; } void ImageStatisticsCalculator::Statistics::SetHotspotRMS(double rms) { m_HotspotRMS = rms; } void ImageStatisticsCalculator::Statistics::SetHotspotMaxIndex(vnl_vector maxIndex) { m_HotspotMaxIndex = maxIndex; } void ImageStatisticsCalculator::Statistics::SetHotspotMinIndex(vnl_vector minIndex) { m_HotspotMinIndex = minIndex; } void ImageStatisticsCalculator::Statistics::SetHotspotIndex(vnl_vector index) { m_HotspotIndex = index; } }