diff --git a/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkBrainMaskExtractionImageFilter.txx b/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkBrainMaskExtractionImageFilter.txx
index 812ac8ffdf..ed41c65f13 100644
--- a/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkBrainMaskExtractionImageFilter.txx
+++ b/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkBrainMaskExtractionImageFilter.txx
@@ -1,443 +1,443 @@
 /*===================================================================
 
 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.
 
 ===================================================================*/
 
 /*===================================================================
 
 This file is based heavily on a corresponding ITK filter.
 
 ===================================================================*/
 #ifndef __itkBrainMaskExtractionImageFilter_txx
 #define __itkBrainMaskExtractionImageFilter_txx
 
 #include "itkBrainMaskExtractionImageFilter.h"
 
 #include <itkBinaryThresholdImageFilter.h>
 #include <itkBinaryErodeImageFilter.h>
 #include <itkBinaryDilateImageFilter.h>
 #include <itkVotingBinaryHoleFillingImageFilter.h>
 #include <itkVotingBinaryIterativeHoleFillingImageFilter.h>
 #include <itkBinaryBallStructuringElement.h>
 #include <itkBinaryCrossStructuringElement.h>
 #include <itkAndImageFilter.h>
 #include <itkRecursiveGaussianImageFilter.h>
 #include <itkMaskImageFilter.h>
 
 namespace itk {
 
   template< class TOutputImagePixelType >
     BrainMaskExtractionImageFilter< TOutputImagePixelType >
     ::BrainMaskExtractionImageFilter()
   {
     // At least 1 inputs is necessary for a vector image.
     // For images added one at a time we need at least six
     this->SetNumberOfRequiredInputs( 1 );
   }
 
   template< class TOutputImagePixelType >
     void BrainMaskExtractionImageFilter< TOutputImagePixelType >
     ::GenerateData()
   {
 
     // gaussian filter
     typename itk::RecursiveGaussianImageFilter<InputImageType,InputImageType>::Pointer gaussian
       = itk::RecursiveGaussianImageFilter<InputImageType,InputImageType>::New();
     gaussian->SetInput( this->GetInput(0) );
     gaussian->SetSigma( 1.0 );
 
     try
     {
       gaussian->Update();
     }
     catch( itk::ExceptionObject &e)
     {
       std::cerr << e;
       return;
     }
 
     // threshold the image
     typename itk::BinaryThresholdImageFilter<InputImageType,OutputImageType>::Pointer threshold =
       itk::BinaryThresholdImageFilter<InputImageType,OutputImageType>::New();
 
     threshold->SetInput( gaussian->GetOutput() );
 
     int seuil = static_cast<int>( ComputeHistogram( gaussian->GetOutput() ) );
     threshold->SetLowerThreshold( seuil );
 
     std::cout << "Thresholding..." << std::flush;
     try
     {
       threshold->Update();
     }
     catch( itk::ExceptionObject &e)
     {
       std::cerr << e;
       return;
     }
     std::cout << "Done." << std::endl;
 
 #ifdef DEBUG_ME
     {
       WriterType::Pointer writer = WriterType::New();
       writer->SetInput( threshold->GetOutput() );
       writer->SetFileName( "AfterThreshold.hdr" );
       writer->Update();
     }
 #endif
 
     // erode to remove background noise
     typedef itk::BinaryBallStructuringElement<int, 3> StructuralElementType;
     StructuralElementType ball;
 
     typename itk::BinaryErodeImageFilter<OutputImageType,OutputImageType,StructuralElementType>::Pointer erode =
       itk::BinaryErodeImageFilter<OutputImageType,OutputImageType,StructuralElementType>::New();
 
     ball.SetRadius( 3 );
 
     erode->SetInput( threshold->GetOutput() );
     erode->SetKernel( ball );
 
     std::cout << "Eroding..." << std::flush;
     try
     {
       erode->Update();
     }
     catch( itk::ExceptionObject &e)
     {
       std::cerr << e;
       return;
     }
     std::cout << "Done." << std::endl;
 
 #ifdef DEBUG_ME
     {
       WriterType::Pointer writer = WriterType::New();
       writer->SetInput( erode->GetOutput() );
       writer->SetFileName( "AfterErode.hdr" );
       writer->Update();
     }
 #endif
 
 
     typedef BinaryCrossStructuringElement<int, 3> CrossType;
 
     typedef BinaryDilateImageFilter<OutputImageType,OutputImageType,CrossType> DilateFilterType;
     typedef AndImageFilter<OutputImageType,OutputImageType,OutputImageType> AndFilterType;
 
     typename OutputImageType::Pointer M0 = threshold->GetOutput();
     typename OutputImageType::Pointer Mn = OutputImageType::New();
     Mn->SetRegions( M0->GetLargestPossibleRegion() );
     Mn->SetSpacing( M0->GetSpacing() );
     Mn->SetOrigin( M0->GetOrigin() );
     Mn->SetDirection( M0->GetDirection() );
     Mn->Allocate();
 
     typename OutputImageType::Pointer Mnplus1 = erode->GetOutput();
 
 
     CrossType cross;
     cross.SetRadius( 1 );
     //unsigned long rad[3]={3,3,3};
 
     //ball2.SetRadius( rad );
 
     std::cout << "Conditional reconstruction..." << std::flush;
     int iter = 0;
     do
     {
       std::cout << "Iteration: " << iter++ << std::endl;
       CopyImage( Mn, Mnplus1);
 
       typename DilateFilterType::Pointer dilater = DilateFilterType::New();
       dilater->SetInput( Mn );
       dilater->SetKernel( cross );
 
       try
       {
         dilater->Update();
       }
       catch( itk::ExceptionObject &e)
       {
         std::cerr << e;
         return;
       }
 
       typename AndFilterType::Pointer andfilter = AndFilterType::New();
       andfilter->SetInput(0, M0);
       andfilter->SetInput(1, dilater->GetOutput() );
 
       try
       {
         andfilter->Update();
       }
       catch( itk::ExceptionObject &e)
       {
         std::cerr << e;
         return;
       }
 
       Mnplus1 = andfilter->GetOutput();
 
       /*
       #ifdef DEBUG_ME
       {
       WriterType::Pointer writer = WriterType::New();
       writer->SetInput( andfilter->GetOutput() );
       char filename[512];
       sprintf( filename, "CondReconstruction_iter_%d.hdr", iter);
       writer->SetFileName( filename );
       writer->Update();
       }
       #endif*/
 
     } while( !CompareImages( Mn, Mnplus1) );
 
     std::cout << "Done." << std::endl;
 
 #ifdef DEBUG_ME
     {
       WriterType::Pointer writer = WriterType::New();
       writer->SetInput( Mn );
       writer->SetFileName( "AfterCondReconstruction.hdr" );
       writer->Update();
     }
 #endif
     // now fill the holes
 
     typename itk::VotingBinaryIterativeHoleFillingImageFilter< OutputImageType >::Pointer filler =
       itk::VotingBinaryIterativeHoleFillingImageFilter< OutputImageType >::New();
     filler->SetInput( Mn );
     filler->SetMaximumNumberOfIterations (1000);
 
     std::cout << "Filling the holes..." << std::flush;
     try
     {
       filler->Update();
     }
     catch( itk::ExceptionObject &e)
     {
       std::cerr << e;
       return;
     }
     std::cout << "Done." << std::endl;
 
     typename OutputImageType::Pointer outputImage =
-      static_cast< OutputImageType * >(this->ProcessObject::GetOutput());
+      static_cast< OutputImageType * >(this->ProcessObject::GetOutput(0));
 
     outputImage->SetSpacing( filler->GetOutput()->GetSpacing() );   // Set the image spacing
     outputImage->SetOrigin( filler->GetOutput()->GetOrigin() );     // Set the image origin
     outputImage->SetLargestPossibleRegion( filler->GetOutput()->GetLargestPossibleRegion());
     outputImage->SetBufferedRegion( filler->GetOutput()->GetLargestPossibleRegion() );
     outputImage->SetDirection( filler->GetOutput()->GetDirection() );
     outputImage->Allocate();
 
     itk::ImageRegionIterator<OutputImageType> itIn( filler->GetOutput(), filler->GetOutput()->GetLargestPossibleRegion() );
     itk::ImageRegionIterator<OutputImageType> itOut( outputImage, outputImage->GetLargestPossibleRegion() );
 
     while( !itIn.IsAtEnd() )
     {
         itOut.Set(itIn.Get());
         ++itIn;
         ++itOut;
     }
 
   }
 
 
   template< class TOutputImagePixelType >
     void BrainMaskExtractionImageFilter< TOutputImagePixelType >
     ::CopyImage( typename OutputImageType::Pointer target, typename OutputImageType::Pointer source)
   {
     itk::ImageRegionConstIterator<OutputImageType> itIn( source, source->GetLargestPossibleRegion() );
     itk::ImageRegionIterator<OutputImageType> itOut( target, target->GetLargestPossibleRegion() );
 
     while( !itOut.IsAtEnd() )
     {
       itOut.Set( itIn.Get() );
       ++itIn;
       ++itOut;
     }
   }
 
 
   template< class TOutputImagePixelType >
     bool BrainMaskExtractionImageFilter< TOutputImagePixelType >
     ::CompareImages( typename OutputImageType::Pointer im1, typename OutputImageType::Pointer im2)
   {
     itk::ImageRegionConstIterator<OutputImageType> itIn( im1, im1->GetLargestPossibleRegion() );
     itk::ImageRegionConstIterator<OutputImageType> itOut( im2, im2->GetLargestPossibleRegion() );
 
     while( !itOut.IsAtEnd() )
     {
       if( itOut.Value() != itIn.Value() )
       {
         return false;
       }
       ++itOut;
       ++itIn;
     }
 
     return true;
   }
 
 
   template< class TOutputImagePixelType >
     int BrainMaskExtractionImageFilter< TOutputImagePixelType >
     ::ComputeHistogram( typename InputImageType::Pointer image)
   {
     // IMPORTANT: IMAGE MUST BE UNSIGNED SHORT
     int N=65535;
     int* histogram = new int[N];
     for( int i=0; i<N; i++)
     {
       histogram[i] = 0;
     }
 
     itk::ImageRegionConstIterator<InputImageType> itIn( image, image->GetLargestPossibleRegion() );
 
     long totVoxels = 0;
 
     int max = -1;
     int min = 9999999;
     while( !itIn.IsAtEnd() )
     {
       histogram[ (int)(itIn.Value()) ]++;
 
       if( itIn.Value()>max )
       {
         max = itIn.Value();
       }
 
       if( itIn.Value()<min )
       {
         min = itIn.Value();
       }
       ++itIn;
       ++totVoxels;
     }
 
 
     //int EPS = 1;
     int seuil = 0;
     //int newseuil = (max + min)/2;
 
     N = max;
 
     double V = 0.0;
     int S = 0;
 
     double mean = 0.0;
     for( int i=min; i<=max; i++)
     {
       mean += (double)(i) * (double)histogram[i];
     }
     mean /= (double)totVoxels;
 
     //std::cout << "Min/Max/Mean: " << min << "/" << max << "/" << mean << std::endl;
 
     //while( abs(newseuil - seuil)>EPS )
     for( seuil = min; seuil<=max; seuil++)
     {
       //seuil = newseuil;
 
       // compute the classes:
       double mean0 = 0.0;
       double mean1 = 0.0;
       //double std0 = 0.0;
       //double std1 = 0.0;
 
       double num0 = 0.0;
       double num1 = 0.0;
       for( int i=min; i<seuil; i++)
       {
         //mean0 += (double)(histogram[i])/(double)(totVoxels) * (double)(i);
         mean0 += (double)histogram[i] * (double)i;
         num0  += (double)histogram[i];
       }
 
       for( int i=seuil; i<max; i++)
       {
         //mean1 += (double)(histogram[i])/(double)(totVoxels) * (double)(i);
         mean1 += (double)histogram[i] * (double)i;
         num1  += (double)histogram[i];
       }
 
       if( num0 )
       {
         mean0/=num0;
       }
       if( num1 )
       {
         mean1/=num1;
       }
 
       /*
       for( int i=0; i<seuil; i++)
       {
       std0 += (double)histogram[i]/(double)totVoxels* ((double)i - mean0)*((double)i - mean0);
       //std0 += (double)histogram[i]/num0* ((double)i - mean0)*((double)i - mean0);
       }
 
       for( int i=seuil; i<65536; i++)
       {
       std1 += (double)histogram[i]/(double)totVoxels* ((double)i - mean1)* ((double)i - mean1);
       //std1 += (double)histogram[i]/num1* ((double)i - mean1)* ((double)i - mean1);
       }
       */
 
 
       //std0 = sqrt(std0);
       //std1 = sqrt(std1);
 
       //std::cout << "Classe 0: " << mean0 << " " << std0 << std::endl;
       //std::cout << "Classe 1: " << mean1 << " " << std1 << std::endl;
 
       //std::cout << "Classe 0: " << mean0 << std::endl;
       //std::cout << "Classe 1: " << mean1 << std::endl;
 
       //double alpha = std0*std1/(std0+std1);
 
       //std::cout << "alpha: " << alpha << " " << std0 << " " << std1 << std::endl;
       //newseuil = (int) (alpha*(mean0/std0 + mean1/std1));
 
       //std::cout << "New threshold: " << newseuil << std::endl;
 
       /*
       if( newseuil > 65535 || newseuil<0)
       {
       std::cerr << "Error: threshold is too low or high, exiting" << std::endl;
       return -1;
       }*/
 
       double Vm = num0 * (mean0 - mean)*(mean0 - mean) + num1*(mean1 - mean)*(mean1 - mean);
       if( Vm > V )
       {
         V = Vm;
         S = seuil;
         //std::cout << "New seuil: " << S << std::endl;
         //getchar();
       }
 
     }
 
     delete [] histogram;
 
     std::cout << "Seuil: " << S << std::endl;
 
     return S;
   }
 }
 
 #endif // __itkBrainMaskExtractionImageFilter_txx