diff --git a/Modules/DiffusionImaging/DiffusionCore/include/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx b/Modules/DiffusionImaging/DiffusionCore/include/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
index 4dfea68105..4058b84022 100644
--- a/Modules/DiffusionImaging/DiffusionCore/include/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
+++ b/Modules/DiffusionImaging/DiffusionCore/include/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
@@ -1,867 +1,867 @@
 /*===================================================================
 
 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 _itk_TensorReconstructionWithEigenvalueCorrectionFilter_txx_
 #define _itk_TensorReconstructionWithEigenvalueCorrectionFilter_txx_
 #endif
 #include "itkImageRegionConstIterator.h"
 #include <math.h>
 #include "itkImageFileWriter.h"
 #include "itkImage.h"
 #include "itkImageRegionIterator.h"
 #include <itkImageDuplicator.h>
 
 namespace itk
 {
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::TensorReconstructionWithEigenvalueCorrectionFilter()
 {
   m_B0Threshold = 50.0;
 }
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 void
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::GenerateData ()
 {
   typename GradientImagesType::Pointer input_image = static_cast< GradientImagesType * >( this->ProcessObject::GetInput(0) );
   typename itk::ImageDuplicator<GradientImagesType>::Pointer duplicator = itk::ImageDuplicator<GradientImagesType>::New();
   duplicator->SetInputImage(input_image);
   duplicator->Update();
   m_GradientImagePointer = duplicator->GetOutput();
 
   typename GradientImagesType::SizeType size = m_GradientImagePointer->GetLargestPossibleRegion().GetSize();
 
   // number of volumes
   int nof = m_GradientDirectionContainer->Size();
 
   // determine the number of b-zero values
   int numberb0=0;
   for(int i=0; i<nof; i++)
   {
     vnl_vector_fixed <double, 3 > vec = m_GradientDirectionContainer->ElementAt(i);
 
     float bval = vec.magnitude();
     bval = bval*bval*m_BValue;
 
     if(bval<100)
       numberb0++;
   }
 
 
   // Matrix to store all diffusion encoding gradients
   vnl_matrix<double> directions(nof-numberb0,3);
 
   m_B0Mask.set_size(nof);
 
 
   int cnt=0;
   for(int i=0; i<nof; i++)
   {
     vnl_vector_fixed <double, 3 > vec = m_GradientDirectionContainer->ElementAt(i);
 
     float bval = vec.magnitude();
     bval = bval*bval*m_BValue;
 
     if(bval<100)
     {
       // the diffusion encoding gradient is approximately zero, wo we are dealing with a non-diffusion weighted volume
       m_B0Mask[i]=1;
     }
     else
     {
       // dealing with a diffusion weighted volume
       m_B0Mask[i]=0;
 
       // set the diffusion encoding gradient to the directions matrix
       directions[cnt][0] = vec[0];
       directions[cnt][1] = vec[1];
       directions[cnt][2] = vec[2];
 
       cnt++;
     }
   }
 
   // looking for maximal norm among gradients.
   // The norm is calculated with use of spectral radius theorem- based on determination of eigenvalue.
 
   vnl_matrix<double> dirsTimesDirsTrans = directions*directions.transpose();
 
   vnl_vector< double> diagonal(nof-numberb0);
   vnl_vector< double> b_vec(nof-numberb0);
   vnl_vector< double> temporary(3);
 
 
   for (int i=0;i<nof-numberb0;i++)
     diagonal[i]=dirsTimesDirsTrans[i][i];
 
   // normalization: free-water method assumes that directions matrix norm 1 is equal to b=1000
   directions=directions*sqrt(m_BValue/1000.0);
 
   //calculation of norms and storing them in vector
 
   dirsTimesDirsTrans = directions*directions.transpose();
 
   for (int i=0;i<nof-numberb0;i++)
     b_vec[i]= dirsTimesDirsTrans[i][i];
 
   // Calculation of so called design matrix that is used to obtain expected signal.
   vnl_matrix<double> H(nof-numberb0, 6);
   vnl_matrix<double> H_org(nof-numberb0, 6);
   vnl_vector<double> pre_tensor(9);
 
   //H is matrix that contains covariances for directions. It is stored twice because its original value is needed later
   // while H is changed
 
   int etbt[6] = { 0, 4, 8, 1, 5, 2 };// tensor order
 
   for (int i = 0; i < nof-numberb0; i++)
   {
     for (int j = 0; j < 3; j++)
     {
       temporary[j] = -directions[i][j];
     }
     for (int j = 0; j < 3; j++)
     {
       for (int k = 0; k < 3; k++)
       {
         pre_tensor[k + 3 * j] = temporary[k] * directions[i][j];
       }
     }
     for (int j = 0; j < 6; j++)
     {
       H[i][j] = pre_tensor[etbt[j]];
     }
     for (int j = 0; j < 3; j++)
     {
       H[i][3 + j] *= 2.0;
     }
   }
 
   H_org=H;
 
   // calculation of inverse matrix by means of pseudoinverse
 
   vnl_matrix<double> inputtopseudoinverse=H.transpose()*H;
   vnl_symmetric_eigensystem<double> eig( inputtopseudoinverse);
   vnl_matrix<double> pseudoInverse = eig.pinverse()*H.transpose();
 
 
   typedef itk::Image<short, 3> MaskImageType;
   MaskImageType::Pointer mask = MaskImageType::New();
   mask->SetRegions(m_GradientImagePointer->GetLargestPossibleRegion().GetSize());
   mask->SetSpacing(m_GradientImagePointer->GetSpacing());
   mask->SetOrigin(m_GradientImagePointer->GetOrigin());
   mask->SetDirection( m_GradientImagePointer->GetDirection() );  // Set the image direction
   mask->SetLargestPossibleRegion( m_GradientImagePointer->GetLargestPossibleRegion() );
   mask->SetBufferedRegion( m_GradientImagePointer->GetLargestPossibleRegion() );
   mask->SetRequestedRegion( m_GradientImagePointer->GetLargestPossibleRegion() );
   mask->Allocate();
 
   // Image thresholding: For every voxel mean B0 image is calculated and then voxels of mean B0 less than the
   // treshold on the B0 image proviced by the userare excluded from the dataset with use of defined mask image.
   // 1 in mask voxel means that B0 > assumed treshold.
 
   int mask_cnt=0;
 #ifdef WIN32
 #pragma omp parallel for
 #else
 #pragma omp parallel for collapse(3)
 #endif
-  for(unsigned int x=0;x<size[0];x++)
-    for(unsigned int y=0;y<size[1];y++)
-      for(unsigned int z=0;z<size[2];z++)
+  for(int x=0;x<size[0];x++)
+    for(int y=0;y<size[1];y++)
+      for(int z=0;z<size[2];z++)
       {
         double mean_b=0.0;
         itk::Index<3> ix = {{x,y,z}};
 
         GradientVectorType pixel = m_GradientImagePointer->GetPixel(ix);
         for (int i=0;i<nof;i++)
         {
           if(m_B0Mask[i]==1)
           {
             mean_b = mean_b + pixel[i];
           }
         }
         mean_b=mean_b/numberb0;
         if(mean_b > m_B0Threshold)
         {
 #pragma omp critical
           {
             mask->SetPixel(ix, 1);
             mask_cnt++;
           }
         }
         else
         {
 #pragma omp critical
           mask->SetPixel(ix, 0);
         }
       }
 
 #ifdef WIN32
 #pragma omp parallel for
 #else
 #pragma omp parallel for collapse(3)
 #endif
-  for (unsigned int x=0;x<size[0];x++)
-    for (unsigned int y=0;y<size[1];y++)
-      for (unsigned int z=0;z<size[2];z++)
+  for (int x=0;x<size[0];x++)
+    for (int y=0;y<size[1];y++)
+      for (int z=0;z<size[2];z++)
       {
         vnl_vector<double> org_vec(nof);
         itk::Index<3> ix = {{x,y,z}};
 
         double mask_val = mask->GetPixel(ix);
 
         GradientVectorType pixel2 = m_GradientImagePointer->GetPixel(ix);
 
         for (int i=0;i<nof;i++)
           org_vec[i]=pixel2[i];
 
         if(mask_val >0)
         {
           for( int f=0;f<nof;f++)
             if(org_vec[f] <= 0)
               org_vec[f] = CheckNeighbours(x,y,z,f,size,mask,m_GradientImagePointer);
 
           for (int i=0;i<nof;i++)
             pixel2[i]=org_vec[i];
 
 #pragma omp critical
           m_GradientImagePointer->SetPixel(ix, pixel2);
         }
       }
 
   typename TensorImageType::Pointer tensorImg;
   tensorImg = TensorImageType::New();
   tensorImg->SetSpacing( m_GradientImagePointer->GetSpacing() );   // Set the image spacing
   tensorImg->SetOrigin( m_GradientImagePointer->GetOrigin() );     // Set the image origin
   tensorImg->SetDirection( m_GradientImagePointer->GetDirection() );  // Set the image direction
   tensorImg->SetLargestPossibleRegion( m_GradientImagePointer->GetLargestPossibleRegion() );
   tensorImg->SetBufferedRegion( m_GradientImagePointer->GetLargestPossibleRegion() );
   tensorImg->SetRequestedRegion( m_GradientImagePointer->GetLargestPossibleRegion() );
   tensorImg->Allocate();
 
   //Declaration of vectors that contains too high or too low atenuation for each gradient. Attenuation is only calculated for
   //non B0 images so nof-numberb0.
 
   vnl_vector< double> pixel_max(nof-numberb0);
   vnl_vector< double> pixel_min(nof-numberb0);
 
   // to high and to low attenuation is calculated with use of highest allowed =5 and lowest allowed =0.01 diffusion coefficient
 
   for (int i=0;i<nof-numberb0;i++)
   {
     pixel_max[i]=exp(-b_vec[i]*0.01);
     pixel_min[i]= exp(-b_vec[i]*5);
   }
 
   m_PseudoInverse = pseudoInverse;
   m_H = H_org;
   m_BVec=b_vec;
 
   // in preprocessing we are dealing with 3 types of voxels: voxels excluded = 0 in mask, voxels correct =1 and voxels under correction
   //= 2. During pre processing most of voxels should be switched from 2 to 1.
 
   // what mask is a variable declared to simplify tensor calculaton. Tensors are obtained only for voxels that have a mask value bigger
   // than what_mask. Sometimes it is 1 sometimes 2.
 
   // initialization of required variables
   double what_mask=1.0;
   double set_mask=2.0;
   double previous_mask=0;
 
   int old_number_negative_eigs=0;
   int new_number_negative_eigs=0;
 
   bool  stil_correcting = true;
 
   TurnMask(size,mask,previous_mask,set_mask);
   // simply defining all possible tensors as with negative eigenvalues
 
   //Preprocessing is performed in multiple iterations as long as the next iteration does not increase the number of bad voxels.
   //The final DWI should be the one that has a smaller or equal number of bad voxels as in the
   //previous iteration. To obtain this temporary DWI image must be stored in memory.
 
   // ! Initial Calculation of Tensors
   std::cout << "Initial tensor calculation" << std::endl;
   GenerateTensorImage(nof,numberb0,size,m_GradientImagePointer,mask,what_mask,tensorImg);
 
   std::cout << "Check negatives" << std::endl;
   // checking how many tensors have problems, this is working only for mask =2
   old_number_negative_eigs = CheckNegatives (size,mask,tensorImg);
 
   //info for the user printed in the consol-debug information to be removed in the future
   std::cout << "Number of negative eigenvalues: " << old_number_negative_eigs << std::endl;
 
   CorrectDiffusionImage(nof,numberb0,size,m_GradientImagePointer,mask,pixel_max,pixel_min);
   while (stil_correcting == true)
   {
     //info for the user printed in the consol-debug information to be removed in the future
     std::cout << "Number of negative eigenvalues: " << old_number_negative_eigs << std::endl;
 
     GenerateTensorImage(nof,numberb0,size,m_GradientImagePointer,mask,what_mask,tensorImg);
 
     new_number_negative_eigs = CheckNegatives (size,mask,tensorImg);
 
     if(new_number_negative_eigs<old_number_negative_eigs)
     {
       // if the correction does not introduce new negative eigenvalues and corrects some of negative detected n previous iteration
       // smoothed DWI is used to substitute DWI from previous iteration
       stil_correcting=true;
       old_number_negative_eigs=new_number_negative_eigs;
 
     }
 
     else
     {
       stil_correcting=false;
     }
 
     //14.10.2013
     //CorrectDiffusionImage(nof,numberb0,size,corrected_diffusion_temp,mask,pixel_max,pixel_min);
     CorrectDiffusionImage(nof,numberb0,size,m_GradientImagePointer,mask,pixel_max,pixel_min);
   }
 
   // Changing the mask value for voxels that are still not corrcted. Original idea is to take them into analysis even though
   // eigenvalues are still negative
   TurnMask(size, mask,1,1);
 
   // Generation of final pre-processed tensor image that might be used as an input for FWE method
 
   // Final Tensor Reconstruction
   std::cout << "Final tensors " << std::endl;
   GenerateTensorImage(nof,numberb0,size,m_GradientImagePointer,mask,what_mask,tensorImg);
 
   m_MaskImage = mask;
 
 
 
   // Change diffusivity representation to standard convention
   itk::ImageRegionIterator<OutputType> outputIterator(tensorImg, tensorImg->GetLargestPossibleRegion());
   outputIterator.GoToBegin();
   while(!outputIterator.IsAtEnd())
   {
     TensorPixelType tens = outputIterator.Get();
 
     tens/= 1000.0;
 
     outputIterator.Set(tens);
     ++outputIterator;
   }
 
   this->SetNthOutput(0, tensorImg);
 }
 
 
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 void
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::SetGradientImage( GradientDirectionContainerType *gradientDirection,
                     const GradientImagesType *gradientImage )
 {
 
   if( m_GradientImageTypeEnumeration == GradientIsInManyImages )
   {
     itkExceptionMacro( << "Cannot call both methods:"
                        << "AddGradientImage and SetGradientImage. Please call only one of them.");
   }
 
   this->m_GradientDirectionContainer = gradientDirection;
 
 
   unsigned int numImages = gradientDirection->Size();
   this->m_NumberOfBaselineImages = 0;
 
   this->m_NumberOfGradientDirections = numImages - this->m_NumberOfBaselineImages;
 
   // ensure that the gradient image we received has as many components as
   // the number of gradient directions
   if( gradientImage->GetVectorLength() != this->m_NumberOfBaselineImages + this->m_NumberOfGradientDirections )
   {
     itkExceptionMacro( << this->m_NumberOfGradientDirections << " gradients + " << this->m_NumberOfBaselineImages
                        << "baselines = " << this->m_NumberOfGradientDirections + this->m_NumberOfBaselineImages
                        << " directions specified but image has " << gradientImage->GetVectorLength()
                        << " components.");
   }
 
   this->ProcessObject::SetNthInput( 0,
                                     const_cast< GradientImagesType* >(gradientImage) );
   m_GradientImageTypeEnumeration = GradientIsInASingleImage;
 }
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 double
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::CheckNeighbours(int x, int y, int z,int f, itk::Size<3> size, itk::Image<short, 3>::Pointer mask, typename GradientImagesType::Pointer corrected_diffusion_temp)
 {
   // method is used for finding a new value for the voxel with use of its 27 neighborhood. To perform such a smoothing correct voxels are
   // counted an arithmetical mean is calculated and stored as a new value for the voxel. If there is no proper neigborhood voxel is turned
   // to the value of 0.
 
   // Definition of neighbourhood avoiding crossing the image boundaries
   int x_max=size[0]-1;
   int y_max=size[1]-1;
   int z_max=size[2]-1;
 
   double back_x=std::max(0,x-1);
   double back_y=std::max(0,y-1);
   double back_z=std::max(0,z-1);
 
   double forth_x=std::min((x+1),x_max);
   double forth_y=std::min((y+1),y_max);
   double forth_z=std::min((z+1),z_max);
 
 
   double tempsum=0;
   double temp_number=0;
 
   for(int i=back_x; i<=forth_x; i++)
   {
     for (int j=back_y; j<=forth_y; j++)
     {
       for (int k=back_z; k<=forth_z; k++)
       {
         itk::Index<3> ix = {{i,j,k}};
 
         GradientVectorType p = corrected_diffusion_temp->GetPixel(ix);
 
         if (p[f] > 0.0 )// taking only positive values and counting them
         {
           if(!(i==x && j==y && k== z))
           {
             tempsum=tempsum+p[f];
             temp_number++;
           }
         }
       }
     }
   }
 
   //getting back to the original position of the voxel
 
   itk::Index<3> ix = {{x,y,z}};
   if (temp_number <= 0.0)
   {
     tempsum=0;
 #pragma omp critical
     mask->SetPixel(ix,0);
   }
   else
   {
     tempsum=tempsum/temp_number;
   }
 
   return tempsum;// smoothed value of voxel
 }
 
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 void
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::CalculateAttenuation(vnl_vector<double> org_data,vnl_vector<double> &atten,int nof, int numberb0)
 {
   double mean_b=0.0;
 
   for (int i=0;i<nof;i++)
     if(m_B0Mask[i]>0)
       mean_b=mean_b+org_data[i];
 
   mean_b=mean_b/numberb0;
   int cnt=0;
   for (int i=0;i<nof;i++)
     if(m_B0Mask[i]==0)
     {
       //if(org_data[i]<0.001){org_data[i]=0.01;}
       atten[cnt]=org_data[i]/mean_b;
       cnt++;
     }
 }
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 double
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::CheckNegatives ( itk::Size<3> size, itk::Image<short, 3>::Pointer mask, typename itk::Image< itk::DiffusionTensor3D<TTensorPixelType>, 3 >::Pointer tensorImg )
 {
   // The method was created to simplif the flow of negative eigenvalue correction process. The method itself just return the number
   // of voxels (tensors) with negative eigenvalues. Then if the voxel was previously bad ( mask=2 ) but it is not bad anymore mask is
   //changed to 1.
 
   // declaration of important structures and variables
   double badvoxels=0;
 
 #ifdef WIN32
 #pragma omp parallel for
 #else
 #pragma omp parallel for collapse(3)
 #endif
-  for (unsigned int x=0;x<size[0];x++)
-    for (unsigned int y=0;y<size[1];y++)
-      for (unsigned int z=0;z<size[2];z++)
+  for (int x=0;x<size[0];x++)
+    for (int y=0;y<size[1];y++)
+      for (int z=0;z<size[2];z++)
       {
         double pixel=0;
 
         itk::Index<3> ix = {{x,y,z}};
         pixel = mask->GetPixel(ix);
 
         // but only if previously marked as bad one-negative eigen value
         if(pixel > 1)
         {
           itk::DiffusionTensor3D<double>::EigenValuesArrayType eigenvalues;
           itk::DiffusionTensor3D<double>::EigenVectorsMatrixType eigenvectors;
           itk::DiffusionTensor3D<double> ten = tensorImg->GetPixel(ix);
           ten.ComputeEigenAnalysis(eigenvalues, eigenvectors);
 
           //comparison to 0.01 instead of 0 was proposed by O.Pasternak
           if( eigenvalues[0]>0.01 && eigenvalues[1]>0.01 && eigenvalues[2]>0.01)
           {
 #pragma omp critical
             mask->SetPixel(ix,1);
           }
           else
           {
 #pragma omp critical
             badvoxels++;
           }
         }
       }
 
   return badvoxels;
 }
 
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 void
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::CorrectDiffusionImage(int nof, int numberb0, itk::Size<3> size, typename GradientImagesType::Pointer corrected_diffusion,itk::Image<short, 3>::Pointer mask,vnl_vector< double> pixel_max,vnl_vector< double> pixel_min)
 {
   // in this method the voxels that has tensor negative eigenvalues are smoothed. Smoothing is done on DWI image.For the voxel
   //detected as bad one, B0 image is smoothed obligatory. All other gradient images are smoothed only when value of attenuation
   //is out of declared bounds for too high or too low attenuation.
 
   // declaration of important variables
 
 #ifdef WIN32
 #pragma omp parallel for
 #else
 #pragma omp parallel for collapse(3)
 #endif
-  for (unsigned int z=0;z<size[2];z++)
-    for (unsigned int x=0;x<size[0];x++)
-      for (unsigned int y=0;y<size[1];y++)
+  for (int z=0;z<size[2];z++)
+    for (int x=0;x<size[0];x++)
+      for (int y=0;y<size[1];y++)
       {
         vnl_vector<double> org_data(nof);
         vnl_vector<double> atten(nof-numberb0);
         double cnt_atten=0;
 
         itk::Index<3> ix = {{x, y, z}};
 
         if(mask->GetPixel(ix) > 1.0)
         {
           GradientVectorType  pt = corrected_diffusion->GetPixel(ix);
 
           for (int i=0;i<nof;i++)
             org_data[i]=pt[i];
 
           double mean_b=0.0;
 
           for (int i=0;i<nof;i++)
             if(m_B0Mask[i]>0)
             {
               mean_b=mean_b+org_data[i];
             }
 
           mean_b=mean_b/numberb0;
           int cnt=0;
           for (int i=0;i<nof;i++)
             if(m_B0Mask[i]==0)
             {
               atten[cnt]=org_data[i]/mean_b;
               cnt++;
             }
 
           cnt_atten=0;
 
           //smoothing certain gradient images that are out of declared constraints
           for (int f=0;f<nof;f++)
           {
             if(m_B0Mask[f]==0)
             {
               if(atten[cnt_atten]<pixel_min[cnt_atten] || atten[cnt_atten]> pixel_max[cnt_atten])
               {
 
                 int x_max=size[0]-1;
                 int y_max=size[1]-1;
                 int z_max=size[2]-1;
 
                 double back_x=std::max(0,(int)x-1);
                 double back_y=std::max(0,(int)y-1);
                 double back_z=std::max(0,(int)z-1);
 
                 double forth_x=std::min(((int)x+1),x_max);
                 double forth_y=std::min(((int)y+1),y_max);
                 double forth_z=std::min(((int)z+1),z_max);
 
                 double tempsum=0;
                 double temp_number=0;
 
                 for(unsigned int i=back_x; i<=forth_x; i++)
                   for (unsigned int j=back_y; j<=forth_y; j++)
                     for (unsigned int k=back_z; k<=forth_z; k++)
                     {
                       itk::Index<3> ix = {{i,j,k}};
 
                       GradientVectorType p = corrected_diffusion->GetPixel(ix);
 
                       if(p[f] > 0.0 && !(i==x && j==y && k== z))
                       {
                         tempsum=tempsum+p[f];
                         temp_number++;
                       }
                     }
 
                 //getting back to the original position of the voxel
                 itk::Index<3> ix = {{x,y,z}};
                 if (temp_number <= 0.0)
                 {
                   tempsum=0;
 #pragma omp critical
                   mask->SetPixel(ix,0);
                 }
                 else
                   tempsum=tempsum/temp_number;
 
                 org_data[f] = tempsum;
               }
 
               cnt_atten++;
             }
 
             //smoothing B0
             if(m_B0Mask[f]==1)
             {
               int x_max=size[0] - 1;
               int y_max=size[1] - 1;
               int z_max=size[2] - 1;
 
               double back_x=std::max(0,(int)x-1);
               double back_y=std::max(0,(int)y-1);
               double back_z=std::max(0,(int)z-1);
 
               double forth_x=std::min(((int)x+1),x_max);
               double forth_y=std::min(((int)y+1),y_max);
               double forth_z=std::min(((int)z+1),z_max);
 
               double tempsum=0;
               double temp_number=0;
 
               for(unsigned int i=back_x; i<=forth_x; i++)
                 for (unsigned int j=back_y; j<=forth_y; j++)
                   for (unsigned int k=back_z; k<=forth_z; k++)
                   {
                     itk::Index<3> ix = {{i,j,k}};
 
                     GradientVectorType p = corrected_diffusion->GetPixel(ix);
 
                     //double test= p[f];
 
                     if (p[f] > 0.0 )// taking only positive values and counting them
                     {
                       if(!(i==x && j==y && k== z))
                       {
                         tempsum=tempsum+p[f];
                         temp_number++;
                       }
                     }
                   }
 
               //getting back to the original position of the voxel
               itk::Index<3> ix = {{x,y,z}};
               if (temp_number <= 0.0)
               {
                 tempsum=0;
 #pragma omp critical
                 mask->SetPixel(ix,0);
               }
               else
               {
                 tempsum=tempsum/temp_number;
               }
 
               org_data[f] = tempsum;
             }
           }
 
           for (int i=0;i<nof;i++)
           {
             pt[i]=org_data[i];
           }
 
 #pragma omp critical
           corrected_diffusion->SetPixel(ix, pt);
         }
         else
         {
           GradientVectorType  pt = corrected_diffusion->GetPixel(ix);
 #pragma omp critical
           corrected_diffusion->SetPixel(ix, pt);
         }
       }
 }
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 void
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::GenerateTensorImage(int nof,int numberb0,itk::Size<3> size,itk::VectorImage<short, 3>::Pointer corrected_diffusion,itk::Image<short, 3>::Pointer mask,double what_mask, typename itk::Image< itk::DiffusionTensor3D<TTensorPixelType>, 3 >::Pointer tensorImg)
 {
   // in this method the whole tensor image is updated with a tensors for defined voxels ( defined by a value of mask);
 
 #ifdef WIN32
 #pragma omp parallel for
 #else
 #pragma omp parallel for collapse(3)
 #endif
-  for (unsigned int x=0;x<size[0];x++)
-    for (unsigned int y=0;y<size[1];y++)
-      for (unsigned int z=0;z<size[2];z++)
+  for (int x=0;x<size[0];x++)
+    for (int y=0;y<size[1];y++)
+      for (int z=0;z<size[2];z++)
       {
         itk::Index<3> ix;
 
         vnl_vector<double> org_data(nof);
         vnl_vector<double> atten(nof-numberb0);
         vnl_vector<double> tensor(6);
         itk::DiffusionTensor3D<double> ten;
         double mask_val=0;
 
         ix[0] = x; ix[1] = y; ix[2] = z;
 
         mask_val= mask->GetPixel(ix);
 
         //Tensors are calculated only for voxels above theshold for B0 image.
         if( mask_val > 0.0 )
         {
 
           // calculation of attenuation with use of gradient image and  and mean B0 image
           GradientVectorType pt = corrected_diffusion->GetPixel(ix);
 
           for (int i=0;i<nof;i++)
           {
             org_data[i]=pt[i];
           }
 
           double mean_b=0.0;
 
           for (int i=0;i<nof;i++)
           {
             if(m_B0Mask[i]>0)
             {
               mean_b=mean_b+org_data[i];
             }
 
           }
           mean_b=mean_b/numberb0;
           int cnt=0;
           for (int i=0;i<nof;i++)
           {
             if (org_data[i]<= 0)
 
             {
               org_data[i]=0.1;
             }
             if(m_B0Mask[i]==0)
             {
               atten[cnt]=org_data[i]/mean_b;
               cnt++;
             }
           }
 
           for (int i=0;i<nof-numberb0;i++)
           {
 
             atten[i]=log((double)atten[i]);
           }
 
           // Calculation of tensor with use of previously calculated inverse of design matrix and attenuation
           tensor = m_PseudoInverse*atten;
 
           ten(0,0) = tensor[0];
           ten(0,1) = tensor[3];
           ten(0,2) = tensor[5];
           ten(1,1) = tensor[1];
           ten(1,2) = tensor[4];
           ten(2,2) = tensor[2];
 
 #pragma omp critical
           tensorImg->SetPixel(ix, ten);
         }
         // for voxels with mask value 0 - tensor is simply 0 ( outside brain value)
         else if (mask_val < 1.0)
         {
           ten(0,0) = 0;
           ten(0,1) = 0;
           ten(0,2) = 0;
           ten(1,1) = 0;
           ten(1,2) = 0;
           ten(2,2) = 0;
 
 #pragma omp critical
           tensorImg->SetPixel(ix, ten);
         }
       }
 
 }// end of Generate Tensor
 
 
 template <class TDiffusionPixelType, class TTensorPixelType>
 void
 TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
 ::TurnMask( itk::Size<3> size, itk::Image<short, 3>::Pointer mask, double previous_mask, double set_mask)
 {
   // The method changes voxels in the mask that poses a certain value with other value.
 
   itk::Index<3> ix;
   double temp_mask_value=0;
 
 #ifdef WIN32
 #pragma omp parallel for
 #else
 #pragma omp parallel for collapse(3)
 #endif
-  for(unsigned int x=0;x<size[0];x++)
-    for(unsigned int y=0;y<size[1];y++)
-      for(unsigned int z=0;z<size[2];z++)
+  for(int x=0;x<size[0];x++)
+    for(int y=0;y<size[1];y++)
+      for(int z=0;z<size[2];z++)
       {
         ix[0] = x; ix[1] = y; ix[2] = z;
         temp_mask_value=mask->GetPixel(ix);
 
         if(temp_mask_value>previous_mask)
         {
 #pragma omp critical
           mask->SetPixel(ix,set_mask);
         }
       }
 }
 
 } // end of namespace