diff --git a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp
index 54a010de0e..a1526b6c26 100644
--- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp
+++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.cpp
@@ -1,1756 +1,1755 @@
 /*===================================================================
 
 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 <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 <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 <itkFFTConvolutionImageFilter.h>
 #include <itkEllipseSpatialObject.h>
 #include <itkSpatialObjectToImageFilter.h>
 #include <itkConstantBoundaryCondition.h>
 #include <itkTimeProbesCollectorBase.h>
 
 #include <itkContinuousIndex.h>
 #include <itkNumericTraits.h>
 #include <list>
 
 //#define DEBUG_HOTSPOTSEARCH
 
 #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>
 
 // 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_HotspotRadius(6.2035049089940),   // radius of a 1cm3 sphere in a isotrope image of 1mm spacings
-  m_CalculateHotspot(true)
+  m_HotspotRadiusInMM(6.2035049089940),   // radius of a 1cm3 sphere in mm
+  m_CalculateHotspot(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()
 {
 }
 
 
 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::SetHotspotRadius(double value)
 {
-  m_HotspotRadius = value;
+  m_HotspotRadiusInMM = value;
 }
 
 double ImageStatisticsCalculator::GetHotspotRadius()
 {
-  return m_HotspotRadius;
+  return m_HotspotRadiusInMM;
 }
 
 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::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() );
     }
   }
 }
 
 
 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 (unsigned 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();
 
   // 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<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 )
   {
     Statistics statistics;
     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.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 (unsigned int i = 0; i<statistics.MaxIndex.size(); i++)
           {
             statistics.MaxIndex[i] = tempMaxIndex[i];
             statistics.MinIndex[i] = tempMinIndex[i];
           }
         }
      }
 // FIX END
 
     // TODO DM: what about different label values? ImageStatisticsCalculator usually calculates statistics sets for EACH label in the given mask
     // TODO DM: it would be more consistent if we calculate hotspot statistics for EACH label, not only for the "unequal 0" label (after all other TODOs)
     /*****************************************************Calculate Hotspot Statistics**********************************************/
 
     if(IsHotspotCalculated())
     {
       // TODO DM: CalculateHotspotStatistics should
       //  1. regard mask
       //  2. calculate a hotspot (and its statistics) per mask label/value
       //  3. use LabelStatisticsImageFilter where possible
       Statistics hotspotStatistics = CalculateHotspotStatistics (adaptedImage.GetPointer(), adaptedMaskImage.GetPointer(), GetHotspotRadius());
       statistics.HotspotMax = hotspotStatistics.HotspotMax;
       statistics.HotspotMin = hotspotStatistics.HotspotMin;
-      statistics.HotspotPeak = hotspotStatistics.HotspotPeak;
+      statistics.HotspotMean = hotspotStatistics.HotspotMean;
       statistics.HotspotMaxIndex = hotspotStatistics.HotspotMaxIndex;
       statistics.HotspotMinIndex = hotspotStatistics.HotspotMinIndex;
-      statistics.HotspotPeakIndex = hotspotStatistics.HotspotPeakIndex;
+      statistics.HotspotIndex = hotspotStatistics.HotspotIndex;
       // TODO DM: add other statistics: N, RMS, ... ; clear role of peak/mean
     }
     statisticsContainer->push_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 <typename TPixel, unsigned int VImageDimension  >
 ImageStatisticsCalculator::MinMaxIndex ImageStatisticsCalculator::CalculateMinMaxIndex(
   const itk::Image<TPixel, VImageDimension> *inputImage,
   itk::Image<unsigned short, VImageDimension> *maskImage)
 {
   typedef itk::Image< TPixel, VImageDimension > ImageType;
   typedef itk::Image< unsigned short, VImageDimension > MaskImageType;
 
   typedef itk::ImageRegionConstIterator<MaskImageType> MaskImageIteratorType;
   typedef itk::ImageRegionConstIteratorWithIndex<ImageType> InputImageIndexIteratorType;
 
   MaskImageIteratorType maskIt(maskImage, maskImage->GetLargestPossibleRegion()); // TODO DM: we should use the same regions here
   InputImageIndexIteratorType imageIndexIt(inputImage, inputImage->GetLargestPossibleRegion());
 
   float maxValue = itk::NumericTraits<float>::min(); // TODO DM: I DID correct this before: use named functions instead of using -
   float minValue = itk::NumericTraits<float>::max();
 
   typename ImageType::IndexType maxIndex;
   typename ImageType::IndexType minIndex;
 
   for(maskIt.GoToBegin(), imageIndexIt.GoToBegin();
     !maskIt.IsAtEnd() && !imageIndexIt.IsAtEnd();
     ++maskIt, ++imageIndexIt)
   {
     if(maskIt.Get() > itk::NumericTraits<typename MaskImageType::PixelType>::Zero) // TODO DM: this is where multiple mask values could be used
     {
       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;
       }
     }
   }
 
   MinMaxIndex 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 <unsigned int VImageDimension>
 itk::SmartPointer< itk::Image<float, VImageDimension> >
 ImageStatisticsCalculator
 ::GenerateHotspotSearchConvolutionMask(double spacing[VImageDimension], double radiusInMM)
 {
   double radiusInMMSquared = radiusInMM * radiusInMM;
   typedef itk::Image< float, VImageDimension > MaskImageType;
   typename MaskImageType::Pointer convolutionMask = MaskImageType::New();
 
   // Calculate size and allocate mask image
   typedef typename MaskImageType::IndexType IndexType;
   IndexType maskIndex;
   maskIndex.Fill(0);
 
   typedef typename MaskImageType::SizeType SizeType;
   SizeType maskSize;
 
   Point3D convolutionMaskCenter; convolutionMaskCenter.Fill(0.0);
   for(unsigned int i = 0; i < VImageDimension; ++i)
   {
     maskSize[i] = ::ceil( 2.0 * radiusInMM / spacing[i] );
 
     // We always need an uneven size to determine a clear center point in the convolution mask // TODO DM: actually this is not true, is it? I don't see a reason
     if(maskSize[i] % 2 == 0 )
     {
       ++maskSize[i];
     }
 
     // TODO DM: center is wrong (below is the corrected calculation)
     // convolutionMaskCenterCoordinate[i] = (maskSize[i] -1) / 2;
     // coordinates are center based: with 1 pixel  the center is at 0.0
     // coordinates are center based: with 2 pixels the center is at 0.5
     // coordinates are center based: with 3 pixels the center is at 1.0
     // coordinates are center based: with 4 pixels the center is at 1.5
     // etc.
     convolutionMaskCenter[i] = 0.5 * (double)(maskSize[i]-1);
   }
 
   typedef typename MaskImageType::RegionType RegionType;
   RegionType maskRegion;
   maskRegion.SetSize(maskSize);
   maskRegion.SetIndex(maskIndex);
 
   convolutionMask->SetRegions(maskRegion);
   convolutionMask->SetSpacing(spacing);
   convolutionMask->Allocate();
 
   // Fill mask image values by subsampling the image grid
   typedef itk::ImageRegionIteratorWithIndex<MaskImageType> MaskIteratorType;
   MaskIteratorType maskIt(convolutionMask,maskRegion);
 
   int numberOfSubVoxelsPerDimension = 2; // per dimension!
   int numberOfSubVoxels = ::pow( static_cast<float>(numberOfSubVoxelsPerDimension), static_cast<float>(VImageDimension) );
   double subVoxelSize = 1.0 / (double)numberOfSubVoxelsPerDimension; //(double)numberOfSubVoxels;
   double valueOfOneSubVoxel = 1.0 / (double)numberOfSubVoxels;
   double maskValue = 0.0;
   Point3D subVoxelPosition;
   double distanceSquared = 0.0;
 
   typedef itk::ContinuousIndex<double, VImageDimension> 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
           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 convolutionMask;
 }
 
 
 // TODO DM: should be refactored into multiple smaller methosd. This one is too large
 template < typename TPixel, unsigned int VImageDimension>
 ImageStatisticsCalculator::Statistics ImageStatisticsCalculator::CalculateHotspotStatistics(
     const itk::Image<TPixel, VImageDimension>* inputImage,
     itk::Image<unsigned short, VImageDimension>* maskImage, // TODO DM: this parameter is completely ignored, although the method is currently ONLY called in the masked input case
     double radiusInMM)
 {
   typedef itk::Image< TPixel, VImageDimension > InputImageType;
   typedef itk::Image< float, VImageDimension > MaskImageType;
 
   double spacing[VImageDimension];
   for (unsigned int dimension = 0; dimension < VImageDimension; ++dimension)
   {
     spacing[dimension] = inputImage->GetSpacing()[dimension];
   }
 
   typename MaskImageType::Pointer convolutionMask = this->GenerateHotspotSearchConvolutionMask<VImageDimension>(spacing, radiusInMM);
 
   typedef typename InputImageType::IndexType IndexType;
   typedef typename InputImageType::SizeType SizeType;
   typedef typename MaskImageType::PointType PointType;
 
   // Convolution of spherical mask and input image
 
   typedef itk::Image< float, VImageDimension > ConvolutionImageType;
   typedef itk::FFTConvolutionImageFilter<InputImageType, MaskImageType, ConvolutionImageType> ConvolutionFilterType; // TODO DM: this line said ConvolutionImageFilter before: why??
   typedef itk::ConstantBoundaryCondition<InputImageType, InputImageType> BoundaryConditionType;
   BoundaryConditionType boundaryCondition;
   boundaryCondition.SetConstant(0.0);
 
   typename ConvolutionFilterType::Pointer convolutionFilter = ConvolutionFilterType::New();
   convolutionFilter->SetBoundaryCondition(&boundaryCondition);
   convolutionFilter->SetInput(inputImage);
   convolutionFilter->SetKernelImage(convolutionMask);
   convolutionFilter->SetNormalize(true);
   convolutionFilter->Update();
   // TODO DM: above Update will calculate the convolution image for ALL of the input image
   // in cases where we have a masked image (always in the first application use case)
   // this is too much! it would be enough to calculate the minimum and maximum index of the mask (in each dimension),
   // in order to define a region for convolutionFilter. This cold save significant time (perhaps enough to fall back to ConvolutionFilterType instead of FFTConvolutionImageFilter)
   // TODO: performance analysis after these changes!
 
   typename ConvolutionImageType::Pointer hotspotImage = convolutionFilter->GetOutput();
   hotspotImage->SetSpacing( inputImage->GetSpacing() ); // TODO: only workaround because convolution filter seems to ignore spacing of input image
 
 
   // TODO DM: why a spatial object? Objective here should be to 1. find position and value of maximum value in convolution image
   /*****************************************************Creating Hotspot Sphere**********************************************/
   typedef itk::Image<unsigned short, VImageDimension> SphereMaskImageType;
   typename SphereMaskImageType::Pointer hotspotSphere = SphereMaskImageType::New();
 
   typedef itk::EllipseSpatialObject<VImageDimension> EllipseType;
   typedef itk::SpatialObjectToImageFilter<EllipseType, SphereMaskImageType> SpatialObjectToImageFilter;
 
-  double hotspotPeak = itk::NumericTraits<double>::min();
+  double hotspotMean = itk::NumericTraits<double>::min();
 
   typename SphereMaskImageType::Pointer croppedRegionMask = SphereMaskImageType::New();
 
   typename SphereMaskImageType::IndexType peakStart;
   peakStart.Fill(0);
   typename SphereMaskImageType::SizeType sphereMaskSize = hotspotImage->GetLargestPossibleRegion().GetSize();
 
   // TODO DM: this creates an image of the input image size!
   typename SphereMaskImageType::RegionType peakRegion;
   peakRegion.SetIndex(peakStart);
   peakRegion.SetSize(hotspotImage->GetLargestPossibleRegion().GetSize());
 
   croppedRegionMask->SetRegions(peakRegion);
   croppedRegionMask->Allocate();
 
   int offsetX = static_cast<int>((radiusInMM / spacing[0]) + 0.99999);
   int offsetY = static_cast<int>((radiusInMM / spacing[1]) + 0.99999);
   int offsetZ = static_cast<int>((radiusInMM / spacing[2]) + 0.99999);
 
   typedef itk::ImageRegionIteratorWithIndex<SphereMaskImageType> CroppedImageIteratorType;
   CroppedImageIteratorType sphereMaskIt(croppedRegionMask, peakRegion);
 
   for(sphereMaskIt.GoToBegin(); !sphereMaskIt.IsAtEnd(); ++sphereMaskIt)
   {
     IndexType index = sphereMaskIt.GetIndex();
 
      if((index[0] >= offsetX && index[0] <= sphereMaskSize[0] - offsetX -1) &&
         (index[1] >= offsetY && index[1] <= sphereMaskSize[1] - offsetY -1) &&
         (index[2] >= offsetZ && index[2] <= sphereMaskSize[2] - offsetZ -1))
         sphereMaskIt.Set(1);
      else
       sphereMaskIt.Set(0);
   }
 
   typedef typename itk::Image<unsigned short, VImageDimension> InputMaskImageType;
   typedef itk::ImageRegionIteratorWithIndex<InputMaskImageType> MaskImageIteratorType;
   MaskImageIteratorType inputMaskIt(maskImage, maskImage->GetLargestPossibleRegion());
   CroppedImageIteratorType sphereMaskIterator(croppedRegionMask, croppedRegionMask->GetLargestPossibleRegion());
 
   for(inputMaskIt.GoToBegin(), sphereMaskIterator.GoToBegin();
       !inputMaskIt.IsAtEnd() &&!sphereMaskIterator.IsAtEnd();
       ++inputMaskIt, ++sphereMaskIterator)
   {
     unsigned int maskValue = inputMaskIt.Get();
     unsigned int sphereMaskValue = sphereMaskIterator.Get();
 
      if(maskValue > 0 && sphereMaskValue > 0)
         sphereMaskIterator.Set(1);
      else
       sphereMaskIterator.Set(0);
   }
 
   // TODO DM: sphereMaskIt seems to define a box region where a sphere could fit inside the input image
   // this seems to come from an idea that Hannes mentioned and what I commented on in line 1244
   // CONVOLUTION should be restricted to an area where we can possibly find result values (i.e. regions inside the mask)
   // in addition, if we require the sphere to be completely contained inside the input image (talk to Mathias/Danial for definition)
   // THEN we should reduce the mask image before working with it (and prior to using it as a bounding region for convolution)
   //
   // Besides the comment above, a spatial object is not useful here. A simple itk::ImageRegion would be enough! (and it would fit into the iterator initialization)
   MinMaxIndex peakInformations = CalculateMinMaxIndex(hotspotImage.GetPointer(), croppedRegionMask.GetPointer());
 
-  // TODO DM: hotspotPeak is irritating: why not only hotspotValue and hotspotIndex? The hotspot IS kind of a peak
-  hotspotPeak = peakInformations.Max;
-  typename SphereMaskImageType::IndexType hotspotPeakIndex;
+  hotspotMean = peakInformations.Max;
+  typename SphereMaskImageType::IndexType hotspotIndex;
   for(int i = 0; i < VImageDimension; ++i)
-    hotspotPeakIndex[i] = peakInformations.MaxIndex[i];
+    hotspotIndex[i] = peakInformations.MaxIndex[i];
 
   typename SphereMaskImageType::SizeType hotspotSphereSize;
   typename SphereMaskImageType::SpacingType hotspotSphereSpacing = inputImage->GetSpacing(); // TODO DM: we don't need a third spacing definition; all our calculations are for one and the same image with just one spacing in variable "spacing"
 
   // TODO DM: remove this and use previously calculated mask size! This is redundant
   for(unsigned int i = 0; i < VImageDimension; ++i)
   {
 
     double countIndex =  2.0 * radiusInMM / hotspotSphereSpacing[i];
 
     // Rounding up to the next integer by cast
     countIndex += 0.9999999;
     int castedIndex = static_cast<int>(countIndex);
 
     // We always have an uneven number in size to determine a center-point in the convolution mask
     if(castedIndex % 2 > 0 )
     {
       hotspotSphereSize[i] = castedIndex;
     }
     else
     {
       hotspotSphereSize[i] = castedIndex +1;
     }
   }
 
   // Initialize SpatialObjectoToImageFilter
   typename itk::SpatialObjectToImageFilter<EllipseType,SphereMaskImageType>::Pointer spatialObjectToImageFilter
     = SpatialObjectToImageFilter::New();
 
   spatialObjectToImageFilter->SetSize(hotspotSphereSize);
   spatialObjectToImageFilter->SetSpacing(hotspotSphereSpacing);
 
   // Creating spatial sphere object
   typename EllipseType::Pointer sphere = EllipseType::New();
   sphere->SetRadius(radiusInMM);
   typedef typename EllipseType::TransformType TransformType;
   typename TransformType::Pointer transform = TransformType::New();
 
   transform->SetIdentity();
 
   typename TransformType::OutputVectorType translation;
 
   // Transform sphere on center-position, set pixelValues inside sphere on 1 and update
   for(int i = 0; i < VImageDimension; ++i)
     translation[i] =  static_cast<int>((hotspotSphereSize[i] -1) * hotspotSphereSpacing[i] / 2);
 
   transform->Translate(translation, false);
 
   sphere->SetObjectToParentTransform(transform);
 
   spatialObjectToImageFilter->SetInput(sphere);
 
   sphere->SetDefaultInsideValue(1.00);
   sphere->SetDefaultOutsideValue(0.00);
 
   spatialObjectToImageFilter->SetUseObjectValue(true);
   spatialObjectToImageFilter->SetOutsideValue(0);
 
   spatialObjectToImageFilter->Update();
   hotspotSphere = spatialObjectToImageFilter->GetOutput();
 
   // Calculate new origin for hotspot sphere
 
   IndexType offsetInIndex;
 
   for(int i = 0; i < VImageDimension; ++i)
     offsetInIndex[i] = hotspotSphereSize[i] / 2;
 
   typename ConvolutionImageType::PointType hotspotOrigin;
-  hotspotImage->TransformIndexToPhysicalPoint(hotspotPeakIndex, hotspotOrigin);
+  hotspotImage->TransformIndexToPhysicalPoint(hotspotIndex, hotspotOrigin);
 
   PointType offsetInPhysicalPoint;
   hotspotSphere->TransformIndexToPhysicalPoint(offsetInIndex, offsetInPhysicalPoint);
 
   for(int i = 0; i < VImageDimension; ++i)
     hotspotOrigin[i] -= offsetInPhysicalPoint[i];
 
   hotspotSphere->SetOrigin(hotspotOrigin);
   hotspotSphere->Allocate();
 
   /* TODO DM: you don't need all of the above "spatial object sphere" code.
      It should be possible to replace all of the below code with a single call
      to your CalculateMinMaxIndex method.
   */
 
 #ifdef DEBUG_HOTSPOTSEARCH
 
       std::cout << std::endl << std::endl;
       std::cout << "hotspotMask: " << std::endl;
       unsigned int lastZ = 1000000000;
       unsigned int lastY = 1000000000;
 
       unsigned int hotspotMaskIndexCounter = 0;
 
       typedef itk::ImageRegionConstIteratorWithIndex<SphereMaskImageType> SphereMaskIteratorType;
       SphereMaskIteratorType hotspotMaskIt(hotspotSphere, hotspotSphere->GetLargestPossibleRegion()  );
 
       for(hotspotMaskIt.GoToBegin();!hotspotMaskIt.IsAtEnd();++hotspotMaskIt)
       {
 
         double tmp = hotspotMaskIt.Get();
         if (hotspotMaskIt.GetIndex()[1] != lastY)
         {
           std::cout << std::endl;
           lastY = hotspotMaskIt.GetIndex()[1];
         }
         if (hotspotMaskIt.GetIndex()[0] != lastZ)
         {
           std::cout << tmp << " ";
           lastZ = hotspotMaskIt.GetIndex()[0];
         }
 
         hotspotMaskIndexCounter++;
 
         if(hotspotMaskIndexCounter > hotspotSphereSize[0] * hotspotSphereSize[1] -1) {
           std::cout << std::endl;
           hotspotMaskIndexCounter = 0;
         }
       }
 
       std::cout << std::endl << std::endl;
 #endif
 
   /*********************************Creating cropped inputImage for calculation of hotspot statistics****************************/
 
   typename InputImageType::IndexType croppedStart;
   hotspotImage->TransformPhysicalPointToIndex(hotspotOrigin,croppedStart);
 
   typename InputImageType::RegionType::SizeType croppedSize = hotspotSphere->GetLargestPossibleRegion().GetSize();
   typename InputImageType::RegionType inputRegion;
   inputRegion.SetIndex(croppedStart);
   inputRegion.SetSize(croppedSize);
 
   typename InputImageType::IndexType croppedOutputStart;
   croppedOutputStart.Fill(0);
 
   typename InputImageType::RegionType croppedOutputRegion;
   croppedOutputRegion.SetIndex(croppedOutputStart);
   croppedOutputRegion.SetSize(hotspotSphere->GetLargestPossibleRegion().GetSize());
 
   typename InputImageType::Pointer croppedOutputImage = InputImageType::New();
   croppedOutputImage->SetRegions(croppedOutputRegion);
   croppedOutputImage->Allocate();
 
   typedef itk::ImageRegionConstIterator<InputImageType> ImageIteratorType;
   ImageIteratorType inputIt(inputImage, inputRegion);
 
   ImageIteratorType croppedOutputImageIt(croppedOutputImage, croppedOutputRegion);
 
   for(inputIt.GoToBegin(), croppedOutputImageIt.GoToBegin(); !inputIt.IsAtEnd(); ++inputIt, ++croppedOutputImageIt)
   {
     croppedOutputImage->SetPixel(croppedOutputImageIt.GetIndex(), inputIt.Get());
   }
 
   // Calculate statistics in Hotspot
   MinMaxIndex hotspotInformations;
   Statistics hotspotStatistics;
 
   hotspotInformations = CalculateMinMaxIndex(croppedOutputImage.GetPointer(), hotspotSphere.GetPointer());
 
   // Add offset to cropped indices
   for(int i = 0; i < VImageDimension; ++i)
   {
     hotspotInformations.MaxIndex[i] += croppedStart[i];
     hotspotInformations.MinIndex[i] += croppedStart[i];
   }
 
   hotspotStatistics.HotspotMin = hotspotInformations.Min;
   hotspotStatistics.HotspotMinIndex = hotspotInformations.MinIndex;
   hotspotStatistics.HotspotMax = hotspotInformations.Max;
   hotspotStatistics.HotspotMaxIndex = hotspotInformations.MaxIndex;
-  hotspotStatistics.HotspotPeak = hotspotPeak;
+  hotspotStatistics.HotspotMean = hotspotMean;
 
-  hotspotStatistics.HotspotPeakIndex.set_size(inputImage->GetImageDimension());
-  for (int i = 0; i< hotspotStatistics.HotspotPeakIndex.size(); ++i)
+  hotspotStatistics.HotspotIndex.set_size(inputImage->GetImageDimension());
+  for (int i = 0; i< hotspotStatistics.HotspotIndex.size(); ++i)
   {
-    hotspotStatistics.HotspotPeakIndex[i] = hotspotPeakIndex[i];
+    hotspotStatistics.HotspotIndex[i] = hotspotIndex[i];
   }
 
   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<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() );
 }
 
 
 }
diff --git a/Modules/ImageStatistics/mitkImageStatisticsCalculator.h b/Modules/ImageStatistics/mitkImageStatisticsCalculator.h
index f51bd3c410..6b48fd18a7 100644
--- a/Modules/ImageStatistics/mitkImageStatisticsCalculator.h
+++ b/Modules/ImageStatistics/mitkImageStatisticsCalculator.h
@@ -1,397 +1,397 @@
 /*===================================================================
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center,
 Division of Medical and Biological Informatics.
 All rights reserved.
 
 This software is distributed WITHOUT ANY WARRANTY; without
 even the implied warranty of MERCHANTABILITY or FITNESS FOR
 A PARTICULAR PURPOSE.
 
 See LICENSE.txt or http://www.mitk.org for details.
 
 ===================================================================*/
 
 #ifndef mitkImageStatisticsCalculator_h
 #define mitkImageStatisticsCalculator_h
 
 #include "mitkImage.h"
 #include "mitkPlanarFigure.h"
 
 // TODO DM: why the ifndef?
 #ifndef __itkHistogram_h
 #include <itkHistogram.h>
 #endif
 
 #include <itkImageRegionIteratorWithIndex.h>
 
 
 #include <vtkSmartPointer.h>
 
 #include "ImageStatisticsExports.h"
 
 namespace mitk
 {
 
 /**
  * \brief Class for calculating statistics and histogram for an (optionally
  * masked) image.
  *
  * Images can be masked by either a label image (of the same dimensions as
  * the original image) or by a closed mitk::PlanarFigure, e.g. a circle or
  * polygon. When masking with a planar figure, the slice corresponding to the
  * plane containing the figure is extracted and then clipped with contour
  * defined by the figure. Planar figures need to be aligned along the main axes
  * of the image (axial, sagittal, coronal). Planar figures on arbitrary
  * rotated planes are not supported.
  *
  * For each operating mode (no masking, masking by image, masking by planar
  * figure), the calculated statistics and histogram are cached so that, when
  * switching back and forth between operation modes without modifying mask or
  * image, the information doesn't need to be recalculated.
  *
  * The class also has the possibility to calculate minimum, maximum, mean
  * and their corresponding indicies in the hottest spot in a given ROI / VOI.
  * The size of the hotspot is defined by a sphere with a radius specified by
  * the user. This procedure is required for the calculation of SUV-statistics
  * in PET-images for example.
  *
  * Note: currently time-resolved and multi-channel pictures are not properly
  * supported.
  */
 class ImageStatistics_EXPORT ImageStatisticsCalculator : public itk::Object
 {
 public:
 
   /**
     TODO DM: document
   */
   enum
   {
     MASKING_MODE_NONE = 0,
     MASKING_MODE_IMAGE = 1,
     MASKING_MODE_PLANARFIGURE = 2
   };
 
   typedef itk::Statistics::Histogram<double> HistogramType;
   typedef HistogramType::ConstIterator HistogramConstIteratorType;
 
   /**
     TODO DM: document
   */
   struct Statistics
   {
     int Label;
     unsigned int N;      //< number of voxels
     double Min;          //< mimimum value
     double Max;          //< maximum value
     double Mean;         //< mean value
     double Median;       //< median value
     double Variance;     //< variance of values // TODO DM: remove, was never filled with values ; check if any calling code within MITK used this member!
     double Sigma;        //< standard deviation of values (== square root of variance)
     double RMS;          //< root means square (TODO DM: check mesning)
     double HotspotMin;   //< mimimum value inside hotspot
     double HotspotMax;   //< maximum value inside hotspot
     double HotspotMean;  //< mean value inside hotspot
     double HotspotSigma; //< standard deviation of values inside hotspot
                          //TODO DM: where is variance? does not make much sense, but should be consistent with usual statistics
                          //TODO DM: same goes for N
                          //TODO DM: same goes for RMS
     double HotspotPeak;  //< TODO DM: should this not replace "mean" the two values could be irritating
     vnl_vector< int > MinIndex;
     vnl_vector< int > MaxIndex;
     vnl_vector<int> HotspotMaxIndex;
     vnl_vector<int> HotspotMinIndex;
-    vnl_vector<int> HotspotPeakIndex; //< TODO DM: couldn't this be named "hotspot index"? We need to clear naming of hotspotmean, hotspotpeakindex, and hotspotpeak
+    vnl_vector<int> HotspotIndex; //< TODO DM: couldn't this be named "hotspot index"? We need to clear naming of hotspotmean, hotspotpeakindex, and hotspotpeak
 
     // TODO DM: make this struct a real class and put this into a constructor
     void Reset() // TODO DM: move to .cpp file (mitk::ImageStatisticsCalculator::Statistics::Reset() {...})
     {
       Label = 0;
       N = 0;
       Min = 0.0;
       Max = 0.0;
       Mean = 0.0;
       Median = 0.0;
       Variance = 0.0;
       Sigma = 0.0;
       RMS = 0.0;
       HotspotMin = 0.0;
       HotspotMax = 0.0;
       HotspotMean = 0.0;
       HotspotPeak = 0.0;
       HotspotSigma = 0.0; // TODO DM: also reset index values! Check that everything is initialized
     }
   };
 
   struct MinMaxIndex // TODO DM: why this structure? could at least be private
   {
     double Max;
     double Min;
     vnl_vector<int> MaxIndex;
     vnl_vector<int> MinIndex;
   };
 
   typedef std::vector< HistogramType::ConstPointer > HistogramContainer;
   typedef std::vector< Statistics > StatisticsContainer;
 
   mitkClassMacro( ImageStatisticsCalculator, itk::Object );
   itkNewMacro( ImageStatisticsCalculator );
 
   /** \brief Set image from which to compute statistics. */
   void SetImage( const mitk::Image *image );
 
   /** \brief Set image for masking. */
   void SetImageMask( const mitk::Image *imageMask );
 
   /** \brief Set planar figure for masking. */
   void SetPlanarFigure( mitk::PlanarFigure *planarFigure );
 
 
   /** \brief Set/Get operation mode for masking */
   void SetMaskingMode( unsigned int mode );
 
   /** \brief Set/Get operation mode for masking */
   itkGetMacro( MaskingMode, unsigned int );
 
   /** \brief Set/Get operation mode for masking */
   void SetMaskingModeToNone();
 
   /** \brief Set/Get operation mode for masking */
   void SetMaskingModeToImage();
 
   /** \brief Set/Get operation mode for masking */
   void SetMaskingModeToPlanarFigure();
 
   /** \brief Set a pixel value for pixels that will be ignored in the statistics */
   void SetIgnorePixelValue(double value);
 
   /** \brief Get the pixel value for pixels that will be ignored in the statistics */
   double GetIgnorePixelValue();
 
   /** \brief Set whether a pixel value should be ignored in the statistics */
   void SetDoIgnorePixelValue(bool doit);
 
   /** \brief Get whether a pixel value will be ignored in the statistics */
   bool GetDoIgnorePixelValue();
 
   /** \brief Sets the radius for the hotspot */
-  void SetHotspotRadius (double hotspotRadiusInMM);
+  void SetHotspotRadius (double hotspotRadiusInMM); // TODO in mm
 
   /** \brief Returns the radius of the hotspot */
-  double GetHotspotRadius();
+  double GetHotspotRadius(); // TODO in mm
 
   /** \brief Sets whether the hotspot should be calculated */
   void SetCalculateHotspot(bool calculateHotspot);
 
   /** \brief Returns true whether the hotspot should be calculated, otherwise false */
   bool IsHotspotCalculated();
 
   /** \brief Compute statistics (together with histogram) for the current
    * masking mode.
    *
    * Computation is not executed if statistics is already up to date. In this
    * case, false is returned; otherwise, true.*/
   virtual bool ComputeStatistics( unsigned int timeStep = 0 );
 
 
   /** \brief Retrieve the histogram depending on the current masking mode.
    *
    * \param label The label for which to retrieve the histogram in multi-label situations (ascending order).
    */
   const HistogramType *GetHistogram(  unsigned int timeStep = 0, unsigned int label = 0 ) const;
 
   /** \brief Retrieve the histogram depending on the current masking mode (for all image labels. */
   const HistogramContainer &GetHistogramVector(  unsigned int timeStep = 0 ) const;
 
   /** \brief Retrieve statistics depending on the current masking mode.
    *
    * \param label The label for which to retrieve the statistics in multi-label situations (ascending order).
    */
   const Statistics &GetStatistics( unsigned int timeStep = 0, unsigned int label = 0 ) const;
 
 
   /** \brief Retrieve statistics depending on the current masking mode (for all image labels). */
   const StatisticsContainer &GetStatisticsVector( unsigned int timeStep = 0 ) const;
 
 
 
 protected:
   typedef std::vector< HistogramContainer > HistogramVector;
   typedef std::vector< StatisticsContainer > StatisticsVector;
 
   typedef std::vector< itk::TimeStamp > TimeStampVectorType;
   typedef std::vector< bool > BoolVectorType;
 
   typedef itk::Image< unsigned short, 3 > MaskImage3DType;
   typedef itk::Image< unsigned short, 2 > MaskImage2DType;
 
   ImageStatisticsCalculator();
 
   virtual ~ImageStatisticsCalculator();
 
   /** \brief Depending on the masking mode, the image and mask from which to
    * calculate statistics is extracted from the original input image and mask
    * data.
    *
    * For example, a when using a PlanarFigure as mask, the 2D image slice
    * corresponding to the PlanarFigure will be extracted from the original
    * image. If masking is disabled, the original image is simply passed
    * through. */
   void ExtractImageAndMask( unsigned int timeStep = 0 );
 
 
   /** \brief If the passed vector matches any of the three principal axes
    * of the passed geometry, the ínteger value corresponding to the axis
    * is set and true is returned. */
   bool GetPrincipalAxis( const Geometry3D *geometry, Vector3D vector,
     unsigned int &axis );
 
   template < typename TPixel, unsigned int VImageDimension >
   void InternalCalculateStatisticsUnmasked(
     const itk::Image< TPixel, VImageDimension > *image,
     StatisticsContainer* statisticsContainer,
     HistogramContainer *histogramContainer );
 
   template < typename TPixel, unsigned int VImageDimension >
   void InternalCalculateStatisticsMasked(
     const itk::Image< TPixel, VImageDimension > *image,
     itk::Image< unsigned short, VImageDimension > *maskImage,
     StatisticsContainer* statisticsContainer,
     HistogramContainer* histogramContainer );
 
   template < typename TPixel, unsigned int VImageDimension >
   void InternalCalculateMaskFromPlanarFigure(
     const itk::Image< TPixel, VImageDimension > *image, unsigned int axis );
 
   template < typename TPixel, unsigned int VImageDimension >
   void InternalMaskIgnoredPixels(
     const itk::Image< TPixel, VImageDimension > *image,
     itk::Image< unsigned short, VImageDimension > *maskImage );
 
  /** \brief Calculates minimum, maximum, mean value and their
   * corresponding indices in a given ROI. As input the function
   * needs an image and a mask. It returns a MinMaxIndex object. */
   template <typename TPixel, unsigned int VImageDimension >
   MinMaxIndex CalculateMinMaxIndex(
     const itk::Image<TPixel, VImageDimension> *inputImage,
     itk::Image<unsigned short, VImageDimension> *maskImage);
 
   /** \brief Calculates the hotspot statistics within a given
   * ROI. As input the function needs an image, a mask which
   * represents the ROI and a radius which defines the size of
   * the sphere. The function returns a Statistics object. */
   template < typename TPixel, unsigned int VImageDimension>
   Statistics CalculateHotspotStatistics(
     const itk::Image<TPixel, VImageDimension> *inputImage,
     itk::Image<unsigned short, VImageDimension> *maskImage,
     double radiusInMM);
 
   /** Connection from ITK to VTK */
   template <typename ITK_Exporter, typename VTK_Importer>
   void ConnectPipelines(ITK_Exporter exporter, vtkSmartPointer<VTK_Importer> importer)
   {
     importer->SetUpdateInformationCallback(exporter->GetUpdateInformationCallback());
 
     importer->SetPipelineModifiedCallback(exporter->GetPipelineModifiedCallback());
     importer->SetWholeExtentCallback(exporter->GetWholeExtentCallback());
     importer->SetSpacingCallback(exporter->GetSpacingCallback());
     importer->SetOriginCallback(exporter->GetOriginCallback());
     importer->SetScalarTypeCallback(exporter->GetScalarTypeCallback());
 
     importer->SetNumberOfComponentsCallback(exporter->GetNumberOfComponentsCallback());
 
     importer->SetPropagateUpdateExtentCallback(exporter->GetPropagateUpdateExtentCallback());
     importer->SetUpdateDataCallback(exporter->GetUpdateDataCallback());
     importer->SetDataExtentCallback(exporter->GetDataExtentCallback());
     importer->SetBufferPointerCallback(exporter->GetBufferPointerCallback());
     importer->SetCallbackUserData(exporter->GetCallbackUserData());
   }
 
   /** Connection from VTK to ITK */
   template <typename VTK_Exporter, typename ITK_Importer>
   void ConnectPipelines(vtkSmartPointer<VTK_Exporter> exporter, ITK_Importer importer)
   {
     importer->SetUpdateInformationCallback(exporter->GetUpdateInformationCallback());
 
     importer->SetPipelineModifiedCallback(exporter->GetPipelineModifiedCallback());
     importer->SetWholeExtentCallback(exporter->GetWholeExtentCallback());
     importer->SetSpacingCallback(exporter->GetSpacingCallback());
     importer->SetOriginCallback(exporter->GetOriginCallback());
     importer->SetScalarTypeCallback(exporter->GetScalarTypeCallback());
 
     importer->SetNumberOfComponentsCallback(exporter->GetNumberOfComponentsCallback());
 
     importer->SetPropagateUpdateExtentCallback(exporter->GetPropagateUpdateExtentCallback());
     importer->SetUpdateDataCallback(exporter->GetUpdateDataCallback());
     importer->SetDataExtentCallback(exporter->GetDataExtentCallback());
     importer->SetBufferPointerCallback(exporter->GetBufferPointerCallback());
     importer->SetCallbackUserData(exporter->GetCallbackUserData());
   }
 
 
   void UnmaskedStatisticsProgressUpdate();
 
   void MaskedStatisticsProgressUpdate();
 
   template <unsigned int VImageDimension>
   itk::SmartPointer< itk::Image<float, VImageDimension> >
   GenerateHotspotSearchConvolutionMask(double spacing[VImageDimension], double radiusInMM);
 
   /** m_Image contains the input image (e.g. 2D, 3D, 3D+t)*/
   mitk::Image::ConstPointer m_Image;
 
   mitk::Image::ConstPointer m_ImageMask;
 
   mitk::PlanarFigure::Pointer m_PlanarFigure;
 
   HistogramVector m_ImageHistogramVector;
   HistogramVector m_MaskedImageHistogramVector;
   HistogramVector m_PlanarFigureHistogramVector;
 
   HistogramType::Pointer m_EmptyHistogram;
   HistogramContainer m_EmptyHistogramContainer;
 
 
   StatisticsVector m_ImageStatisticsVector;
   StatisticsVector m_MaskedImageStatisticsVector;
   StatisticsVector m_PlanarFigureStatisticsVector;
   StatisticsVector m_MaskedImageHotspotStatisticsVector;
 
   Statistics m_EmptyStatistics;
   StatisticsContainer m_EmptyStatisticsContainer;
 
   unsigned int m_MaskingMode;
   bool m_MaskingModeChanged;
 
   /** m_InternalImage contains a image volume at one time step (e.g. 2D, 3D)*/
   mitk::Image::ConstPointer m_InternalImage;
   MaskImage3DType::Pointer m_InternalImageMask3D;
   MaskImage2DType::Pointer m_InternalImageMask2D;
 
   TimeStampVectorType m_ImageStatisticsTimeStampVector;
   TimeStampVectorType m_MaskedImageStatisticsTimeStampVector;
   TimeStampVectorType m_PlanarFigureStatisticsTimeStampVector;
 
   BoolVectorType m_ImageStatisticsCalculationTriggerVector;
   BoolVectorType m_MaskedImageStatisticsCalculationTriggerVector;
   BoolVectorType m_PlanarFigureStatisticsCalculationTriggerVector;
 
   double m_IgnorePixelValue;
   bool m_DoIgnorePixelValue;
   bool m_IgnorePixelValueChanged;
 
-  double m_HotspotRadius;
+  double m_HotspotRadiusInMM;
   bool m_CalculateHotspot;
 
   unsigned int m_PlanarFigureAxis;    // Normal axis for PlanarFigure
   unsigned int m_PlanarFigureSlice;   // Slice which contains PlanarFigure
   int m_PlanarFigureCoordinate0;      // First plane-axis for PlanarFigure
   int m_PlanarFigureCoordinate1;      // Second plane-axis for PlanarFigure
 
 };
 
 } // namespace
 
 #endif