diff --git a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp
index b8c9038c31..90826835da 100644
--- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp
+++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp
@@ -1,1474 +1,1420 @@
 /*===================================================================
 
 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 <itkScalarImageToHistogramGenerator.h>
 
 #include <itkChangeInformationImageFilter.h>
 #include <itkExtractImageFilter.h>
 #include <itkMinimumMaximumImageCalculator.h>
 #include <itkStatisticsImageFilter.h>
 #include <itkLabelStatisticsImageFilter.h>
 #include <itkMaskImageFilter.h>
 #include <itkImageRegionConstIterator.h>
 #include <itkImageRegionIterator.h>
 #include <itkImageRegionConstIteratorWithIndex.h>
 
 #include <itkCastImageFilter.h>
 #include <itkImageFileWriter.h>
 #include <itkVTKImageImport.h>
 #include <itkVTKImageExport.h>
 
 
 #include <vtkPoints.h>
 #include <vtkCellArray.h>
 #include <vtkPolyData.h>
 #include <vtkLinearExtrusionFilter.h>
 #include <vtkPolyDataToImageStencil.h>
 #include <vtkImageStencil.h>
 #include <vtkImageImport.h>
 #include <vtkImageExport.h>
 #include <vtkImageData.h>
 
 #include <itkImageFileWriter.h>
 #include <itkRescaleIntensityImageFilter.h>
 #include <itkConvolutionImageFilter.h>
 
 #include <list>
 
 #define _USE_MATH_DEFINES
 #include <math.h>
 
 #if ( ( VTK_MAJOR_VERSION <= 5 ) && ( VTK_MINOR_VERSION<=8)  )
   #include "mitkvtkLassoStencilSource.h"
 #else
   #include "vtkLassoStencilSource.h"
 #endif
 
 
 #include <vtkMetaImageWriter.h>
 
 #include <exception>
 
 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)
 {
   m_EmptyHistogram = HistogramType::New();
   m_EmptyHistogram->SetMeasurementVectorSize(1);
   HistogramType::SizeType histogramSize(1);
   histogramSize.Fill( 256 );
   m_EmptyHistogram->Initialize( histogramSize );
 
   m_EmptyStatistics.Reset();
 }
 
 
 ImageStatisticsCalculator::~ImageStatisticsCalculator()
 {
 }
 
 
 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::SetHotspotSize(double value)
 {
   m_HotspotSize = (4/3) * M_PI * value * value * value;
 }
 
 double ImageStatisticsCalculator::GetHotspotSize()
 {
   return m_HotspotSize;
 }
 
 void ImageStatisticsCalculator::SetCalculateHotspot(bool value)
 {
   m_CalculateHotspot = value;
 }
 
 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_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;
       return true;
     }
     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::Statistics & ImageStatisticsCalculator::GetHotspotStatistics( unsigned int timeStep, unsigned int label) const
 {
   if(m_Image.IsNull() || (timeStep >= m_Image->GetTimeSteps()) )
     return m_EmptyStatistics;
 
   return m_MaskedImageHotspotStatisticsVector[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
       ExtractImageFilter::Pointer imageExtractor = ExtractImageFilter::New();
       imageExtractor->SetInput( timeSliceImage );
       imageExtractor->SetSliceDimension( axis );
       imageExtractor->SetSliceIndex( slice );
       imageExtractor->Update();
       m_InternalImage = imageExtractor->GetOutput();
 
 
       // 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() );
     }
   }
 }
 
 
 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.Label = 1;
   statistics.N = image->GetBufferedRegion().GetNumberOfPixels();
   statistics.Min = statisticsFilter->GetMinimum();
   statistics.Max = statisticsFilter->GetMaximum();
   statistics.Mean = statisticsFilter->GetMean();
   statistics.Median = 0.0;
   statistics.Sigma = statisticsFilter->GetSigma();
   statistics.RMS = sqrt( statistics.Mean * statistics.Mean + statistics.Sigma * statistics.Sigma );
 
   statistics.MinIndex.set_size(image->GetImageDimension());
   statistics.MaxIndex.set_size(image->GetImageDimension());
   for (int i=0; i<statistics.MaxIndex.size(); i++)
   {
       statistics.MaxIndex[i] = minMaxFilter->GetIndexOfMaximum()[i];
       statistics.MinIndex[i] = minMaxFilter->GetIndexOfMinimum()[i];
   }
 
   statisticsContainer->push_back( statistics );
 
    // Calculate histogram
   typename HistogramGeneratorType::Pointer histogramGenerator = HistogramGeneratorType::New();
   histogramGenerator->SetInput( image );
   histogramGenerator->SetMarginalScale( 100 );
   histogramGenerator->SetNumberOfBins( 768 );
   histogramGenerator->SetHistogramMin( statistics.Min );
   histogramGenerator->SetHistogramMax( statistics.Max );
   histogramGenerator->Compute();
 
   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<MaskImageType>
       itmask(maskImage, maskImage->GetLargestPossibleRegion());
   itk::ImageRegionConstIterator<ImageType>
       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<double, VImageDimension> 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;
     }
   }
 
   if ( maskNonEmpty )
   {
     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 statistics;
       statistics.Label = (*it);
       statistics.N = labelStatisticsFilter->GetCount( *it );
       statistics.Min = labelStatisticsFilter->GetMinimum( *it );
       statistics.Max = labelStatisticsFilter->GetMaximum( *it );
       statistics.Mean = labelStatisticsFilter->GetMean( *it );
       statistics.Median = labelStatisticsFilter->GetMedian( *it );
       statistics.Sigma = labelStatisticsFilter->GetSigma( *it );
       statistics.RMS = sqrt( statistics.Mean * statistics.Mean
         + statistics.Sigma * statistics.Sigma );
 
       // 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.Min+statistics.Max)/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() );
 
         statistics.MinIndex.set_size(adaptedImage->GetImageDimension());
         statistics.MaxIndex.set_size(adaptedImage->GetImageDimension());
 
         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.
         if (m_MaskingMode == MASKING_MODE_PLANARFIGURE && m_Image->GetDimension()==3)
         {
             statistics.MaxIndex.set_size(m_Image->GetDimension());
             statistics.MaxIndex[m_PlanarFigureCoordinate0]=tempMaxIndex[0];
             statistics.MaxIndex[m_PlanarFigureCoordinate1]=tempMaxIndex[1];
             statistics.MaxIndex[m_PlanarFigureAxis]=m_PlanarFigureSlice;
 
             statistics.MinIndex.set_size(m_Image->GetDimension());
             statistics.MinIndex[m_PlanarFigureCoordinate0]=tempMinIndex[0];
             statistics.MinIndex[m_PlanarFigureCoordinate1]=tempMinIndex[1];
             statistics.MinIndex[m_PlanarFigureAxis]=m_PlanarFigureSlice;
         } else
         {
           for (int i = 0; i<statistics.MaxIndex.size(); i++)
           {
             statistics.MaxIndex[i] = tempMaxIndex[i];
             statistics.MinIndex[i] = tempMinIndex[i];
           }
         }
 // FIX END
       Statistics hotspotStatistics = CalculateMinMaxIndex<TPixel, VImageDimension >(adaptedImage.GetPointer(), adaptedMaskImage.GetPointer());
       statistics.HotspotMean = hotspotStatistics.HotspotMean;
       statistics.HotspotMax = hotspotStatistics.HotspotMax;
       statistics.HotspotMin = hotspotStatistics.HotspotMin;
       statistics.HotspotMaxIndex = hotspotStatistics.HotspotMaxIndex;
       statistics.HotspotMinIndex = hotspotStatistics.HotspotMinIndex;
       statisticsContainer->push_back( statistics );
     }
   }
   else
   {
     histogramContainer->push_back( HistogramType::ConstPointer( m_EmptyHistogram ) );
     statisticsContainer->push_back( Statistics() );
   }
 }
 
 template < typename TPixel, unsigned int VImageDimension>
 ImageStatisticsCalculator::Statistics ImageStatisticsCalculator::CalculateMinMaxIndex(
   const itk::Image<TPixel, VImageDimension> *inputImage,
   itk::Image<unsigned short, VImageDimension> *maskImage)
 {
   // TODO: check if inputImage and maskImage have same dimensions
   typedef itk::Image< TPixel, VImageDimension > ImageType;
   typedef itk::Image< unsigned short, VImageDimension > MaskImageType;
 
   typedef itk::ImageRegionConstIterator<MaskImageType> MaskImageIteratorType;
   typedef itk::ImageRegionConstIteratorWithIndex<ImageType> InputImageIndexIteratorType;
 
   StatisticsContainer statisticsContainer;
 
   MaskImageIteratorType maskIt(maskImage, maskImage->GetLargestPossibleRegion());
   InputImageIndexIteratorType imageIndexIt(inputImage, inputImage->GetLargestPossibleRegion());
 
   float maxValue = 1;
   float minValue = 10000;
 
   ImageType::IndexType maxIndex; // TODO: initialize
   ImageType::IndexType minIndex;
 
   double sumPixelValue = 0;
   unsigned int countPixel = 0;
 
   for(maskIt.GoToBegin(), imageIndexIt.GoToBegin();
       !maskIt.IsAtEnd() && !imageIndexIt.IsAtEnd();
       ++maskIt, ++imageIndexIt)
   {
     if(maskIt.Get() > itk::NumericTraits<typename MaskImageType::PixelType>::Zero) // TODO: was ist der Unterschied zwischen itk::NumericTraits<typename MaskImageType::PixelType>::Zero und "0"
     {
       //Calculate coefficients for Mean-Value
       sumPixelValue = sumPixelValue + imageIndexIt.Get();
       countPixel++;
 
       //Calculate Min and Max and corresponding Index-Values
       if(imageIndexIt.Get() > maxValue)
       {
        maxIndex = imageIndexIt.GetIndex();
        maxValue = imageIndexIt.Get();
       }
 
       if(imageIndexIt.Get() < minValue)
       {
        minIndex = imageIndexIt.GetIndex();
        minValue = imageIndexIt.Get();
       }
     }
   }
 
   Statistics stats;
   stats.HotspotMinIndex.set_size(inputImage->GetImageDimension());
   stats.HotspotMaxIndex.set_size(inputImage->GetImageDimension());
 
   for (int i = 0; i<stats.HotspotMinIndex.size(); i++)
   {
     stats.HotspotMaxIndex[i] = maxIndex[i];
     stats.HotspotMinIndex[i] = minIndex[i];
   }
 
   stats.HotspotMean = sumPixelValue / countPixel;
   stats.HotspotMax = maxValue;
   stats.HotspotMin = minValue;
   return stats;
 }
 template < typename TPixel, unsigned int VImageDimension>
 void ImageStatisticsCalculator::CalculateHotspotStatistics(
     const itk::Image<TPixel, VImageDimension> inputImage,
     itk::Image<unsigned short, VImageDimension> *maskImage,
     double RadiusInMM)
 {
   typedef itk::Image< TPixel, VImageDimension > ImageType;
   typedef itk::Image< unsigned short, VImageDimension > MaskImageType;
 
-  typedef itk::ImageRegionIterator<ImageType> InputImageIteratorType;
+  typedef itk::ImageConstIteratorWithIndex<ImageType> IteratorType;
 
   typedef typename ImageType::SpacingType SpacingType;
   typedef typename ImageType::SizeType SizeType;
   typedef typename ImageType::IndexType IndexType;
   typedef typename ImageType::PointType PointType;
   typedef typename ImageType::RegionType RegionType;
 
   ImageType::Pointer ConvolutionMask = ImageType::New();
 
   Sizetype spacing = inputImage->GetSpacing();
 
   IndexType start;
   start[0] = 0;
   start[1] = 0;
   start[2] = 0;
 
-  SizeType size; //TODO: ob 1 rauskommt, float statt int
-  size[0] = spacing[0] * RadiusInMM * 2.0;
-  size[1] = spacing[1] * RadiusInMM * 2.0;
-  size[2] = spacing[2] * RadiusInMM * 2.0;
+  SizeType size;
+  size[0] = 2.0 * RadiusInMM / spacing[0];
+  size[1] = 2.0 * RadiusInMM / spacing[1];
+  size[2] = 2.0 * RadiusInMM / spacing[2];
 
   PointType pixelCoordinate;
   pixelCoordinate[0] = 0.0;
   pixelCoordinate[1] = 0.0;
   pixelCoordinate[2] = 0.0;
 
   RegionType region;
   region.SetSize(size);
   region.SetIndex(start);
 
   Sizetype spacing = inputImage->GetSpacing();
 
   ConvolutionMask->SetRegions(region);
-  ConvolutionMask->SetOrigin(pixelCoordinate);
+  ConvolutionMask->SetrOrigin(pixelCoordinate);
   ConvolutionMask->SetSpacing(spacing);
   ConvolutionMask->Allocate();
 
-  const double ConvolutionMaskOriginX = size[0]/2;
-  const double ConvolutionMaskOriginY = size[1]/2;
-  const double ConvolutionMaskOriginZ = size[2]/2;
+  PointType convolutionMaskCenterCoordinate;
+  convolutionMaskCenterCoordinate[0] = size[0]/2.00;
+  convolutionMaskCenterCoordinate[1] = size[1]/2.00;
+  convolutionMaskCenterCoordinate[2] = size[2]/2.00;
 
-  typename ImageType::RegionType inputRegionOfInterest = inputImage->GetLargestPossibleRegion();
-  InputImageIteratorType imageIt(inputImage, inputRegionOfInterest);
-
-  IndexType pixelIndex;
+  float subPixelDimensionX = spacing[0]/2;
+  float subPixelDimensionY = spacing[1]/2;
+  float subPixelDimensionZ = spacing[2]/2;
 
   int countSubPixel = 0;
 
-  /*
-  for (all pixels in convolution-kernel)
-    for (all sub-pixels in pixel)
-      if (distance (sub-pixel-center - convolution-kernel-pixel-center) < radius)
-        count sub-pixel as part of mask
-    value of convolution-kernel at position of convolution-kernel-pixel = 1 / sub-pixel-count(in-radius)
-
-    for (all sub-pixels in pixel):
-
-    subpixel-dimension-x = x-spacing / 2;
-    subpixel-dimension-y =
-    subpixel-dimension-y =
-    for ( x = pixel-center - subpixel-dimension-x/2;
-          x <= pixel-center + subpixel-dimension-x/2;
-          x += subpixel-dimension-x )
-      for (y = ...)
-        for (z = ...)
-          if ( (x,y,z) - (center) < radius ) .. count sub-pixel
+  IteratorType maskIt(convolutionMask,convolutionMask->GetLargestPossibleRegion());
 
-  */
-
-  for(imageIt.GoToBegin(); !imageIt.IsAtEnd(); imageIt++)
+  for(maskIt.GoToBegin(); !maskIt.IsAtEnd(); ++maskIt)
   {
-    //Calculate first Subpixel (left upper corner of Subpixel)
-    pixelCoordinate[0] -= spacing[0]/4;
-    pixelCoordinate[1] += spacing[1]/4;
-
-    //Compute distance between Subpixel and origin of ConvolutionMask
-    double distanceSubPixelX = pixelCoordinate[0] - ConvolutionMaskOriginX;
-    double distanceSubPixelY = pixelCoordinate[1] - ConvolutionMaskOriginY;
-    double distanceSubPixelFromOrigin = sqrt(distanceSubPixelX * distanceSubPixelX + distanceSubPixelY * distanceSubPixelY);
-
-    //Check if Subpixel is in the sphere and increase countSubPixel by 1
-    if(distanceSubPixelFromOrigin <= RadiusInMM)
-      countSubPixel++;
-
-    //Calculate second Subpixel (right upper corner of Subpixel)
-    pixelCoordinate[0] += spacing[0]/2;
-    distanceSubPixelX = pixelCoordinate[0] - ConvolutionMaskOriginX;
-    distanceSubPixelY = pixelCoordinate[1] - ConvolutionMaskOriginY;
-    distanceSubPixelFromOrigin = sqrt(distanceSubPixelX * distanceSubPixelX + distanceSubPixelY * distanceSubPixelY);
-
-    if(distanceSubPixelFromOrigin <= RadiusInMM)
-      countSubPixel++;
-
-    //Calculate third Subpixel (right upper corner of Subpixel)
-    pixelCoordinate[1] -= spacing[0]/2;
-    distanceSubPixelX = pixelCoordinate[0] - ConvolutionMaskOriginX;
-    distanceSubPixelY = pixelCoordinate[1] - ConvolutionMaskOriginY;
-    distanceSubPixelFromOrigin = sqrt(distanceSubPixelX * distanceSubPixelX + distanceSubPixelY * distanceSubPixelY);
-
-    if(distanceSubPixelFromOrigin <= RadiusInMM)
-      countSubPixel++;
-
-    //Calculate 4th Subpixel (right upper corner of Subpixel)
-    pixelCoordinate[0] -= spacing[0]/2;
-    distanceSubPixelX = pixelCoordinate[0] - ConvolutionMaskOriginX;
-    distanceSubPixelY = pixelCoordinate[1] - ConvolutionMaskOriginY;
-    distanceSubPixelFromOrigin = sqrt(distanceSubPixelX * distanceSubPixelX + distanceSubPixelY * distanceSubPixelY);
-
-    if(distanceSubPixelFromOrigin <= RadiusInMM)
-      countSubPixel++;
+    for(float x = pixelCoordinate[0] - subPixelDimensionX / 2;
+        x <= pixelCoordinate[0] + subPixelDimensionX / 2;
+        x += subPixelDimensionX)
+    {
+      for(float y = pixelCoordinate[1] - subPixelDimensionY /2;
+          y <= pixelCoordinate[1] + subPixelDimensionY / 2;
+          y += subPixelDimensionY)
+      {
+        for(float z = pixelCoordinate[2] - subPixelDimensionZ / 2;
+            z <= pixelCoordinate[2] - subPixelDimensionZ / 2;
+            z += subPixelDimensionZ)
+        {
+          PointType subPixelCenterCoordinate;
+          subPixelCenterCoordinate[0] = x;
+          subPixelCenterCoordinate[1] = y;
+          subPixelCenterCoordinate[2] = z;
 
-    //Divide counted Subpixels by 4 and assign this value the pixel
-    int pixelValue = countSubPixel/4;
+          if(subPixelCoordinate.EuclideanDistanceTo(convolutionMaskCenterCoordinate) <= RadiusInMM)
+            countSubPixel++;
+        }
+      }
+    }
+    int pixelValue = countSubPixel/8;
 
-    bool isInside = image->TransformPhysicalPointToIndex(pixelCoordinate, pixelIndex)
+    bool isInside = image->TransformPhysicalPointToIndex(pixelCoordinate, maskIt.GetIndex())
 
     if(isInside)
     {
       ConvolutionMask->SetPixel(pixelIndex,pixelValue);
     }
 
     countSubPixel = 0;
   }
-
-  //Convolution of InputImage and ConvolutionMask
-  typedef itk::ConvolutionImageFilte<ImageType> FilterType;
-  FilterType::Pointer convolutionFilter = FilterType::New();
-
-  convolutionFilter->SetInput(inputImage);
-  convolutionFilter->SetKernelImage(ConvolutionMask);
-
-  //Create Outputimage (PeakImage)
-  convolutionFilter->UpdateLargestPossibleRegion();
-
-  ImageType::Pointer PeakImage = convolutionFilter->GetOutput();
-
-  //Calculate Statistics from PeakImage
-  //CalculateMinMaxIndex(PeakImage.GetPointer(), maskImage);
-
 }
 
 
 
 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<vtkPoints> points = vtkSmartPointer<vtkPoints>::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<vtkLassoStencilSource> lassoStencil = vtkSmartPointer<vtkLassoStencilSource>::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<vtkImageImport> vtkImporter = vtkSmartPointer<vtkImageImport>::New();
   this->ConnectPipelines( itkExporter, vtkImporter );
 
   // Apply the generated image stencil to the input image
   vtkSmartPointer<vtkImageStencil> imageStencilFilter = vtkSmartPointer<vtkImageStencil>::New();
   imageStencilFilter->SetInputConnection( vtkImporter->GetOutputPort() );
   imageStencilFilter->SetStencil( lassoStencil->GetOutput() );
   imageStencilFilter->ReverseStencilOff();
   imageStencilFilter->SetBackgroundValue( 0 );
   imageStencilFilter->Update();
 
   // Export from VTK back to ITK
   vtkSmartPointer<vtkImageExport> vtkExporter = vtkImageExport::New(); // TODO: this is WRONG, should be vtkSmartPointer<vtkImageExport>::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() );
 }
 
 
 }