C++ Pointer::GetLargestPossibleRegion方法代码示例

    void operator()(Parameters& params)
    {
        typedef typename ::itk::Image< PIXELTYPE, 3 > ImageType;
        const typename ImageType::Pointer itkImage = ::fwItkIO::itkImageFactory< ImageType >(params.i_image);

        typename ::itk::ResampleImageFilter<ImageType, ImageType>::Pointer resampler =
            ::itk::ResampleImageFilter<ImageType, ImageType>::New();

        typename ::itk::MinimumMaximumImageCalculator< ImageType >::Pointer minCalculator =
            ::itk::MinimumMaximumImageCalculator< ImageType >::New();

        minCalculator->SetImage(itkImage);
        minCalculator->ComputeMinimum();
        resampler->SetDefaultPixelValue(minCalculator->GetMinimum());

        resampler->SetTransform(params.i_trf.GetPointer());
        resampler->SetInput(itkImage);

        typename ImageType::SizeType size           = itkImage->GetLargestPossibleRegion().GetSize();
        typename ImageType::PointType origin        = itkImage->GetOrigin();
        typename ImageType::SpacingType spacing     = itkImage->GetSpacing();
        typename ImageType::DirectionType direction = itkImage->GetDirection();

        SLM_ASSERT("Input spacing can't be null along any axis", spacing[0] > 0 && spacing[1] > 0 && spacing[2] > 0);

        if(params.i_targetImage)
        {
            for(std::uint8_t i = 0; i < 3; ++i)
            {
                // ITK uses unsigned long to store sizes.
                size[i] = static_cast<typename ImageType::SizeType::SizeValueType>(params.i_targetImage->getSize()[i]);

                origin[i]  = params.i_targetImage->getOrigin()[i];
                spacing[i] = params.i_targetImage->getSpacing()[i];

                SLM_ASSERT("Output spacing can't be null along any axis.", spacing[i] > 0);
            }
        }

        resampler->SetSize(size);
        resampler->SetOutputOrigin(origin);
        resampler->SetOutputDirection(direction);
        resampler->SetOutputSpacing(spacing);

        resampler->Update();

        typename ImageType::Pointer outputImage = resampler->GetOutput();

        ::fwItkIO::itkImageToFwDataImage(outputImage, params.o_image);
    }

void WorkbenchUtils::addPaddingItk(itk::Image <PixelType, ImageDimension> *itkImage, Axis axis, bool append,
                                   int numberOfSlices, float pixelValue, Image::Pointer outImage) {
    // pixel type is templated. The input field for the value is set to float, so the user might enter some invalid values for the image type at hand.
    // since all primitive built-in types have well defined casting behaviour between each other, we'll just do a typecast. we will clip the entered
    // value at PixelTypes min/max to prevent an overflow. The possible loss of precision is ignored.
    float lower = itk::NumericTraits<PixelType>::min();
    float upper = itk::NumericTraits<PixelType>::max();
    float clippedPixelValue = std::max(lower, std::min(pixelValue, upper));

    PixelType paddingPixelValue = (PixelType) clippedPixelValue;

    typedef itk::Image <PixelType, ImageDimension> ImageType;

    // gather all data
    typename ImageType::SizeType lowerBound;
    typename ImageType::SizeType upperBound;
    lowerBound.Fill(0);
    upperBound.Fill(0);

    unsigned int itkAxis = convertToItkAxis(axis);
    if (append) {
        upperBound[itkAxis] = numberOfSlices;
    } else {
        lowerBound[itkAxis] = numberOfSlices;
    }

    // setup the filter
    typedef itk::ConstantPadImageFilter <ImageType, ImageType> PadFilterType;
    typename PadFilterType::Pointer padFilter = PadFilterType::New();
    padFilter->SetInput(itkImage);
    padFilter->SetConstant(paddingPixelValue);
    padFilter->SetPadLowerBound(lowerBound);
    padFilter->SetPadUpperBound(upperBound);
    padFilter->UpdateLargestPossibleRegion();

    // Update the origin, since padding creates negative index that is lost when returned to MITK
    typename ImageType::Pointer paddedImage = padFilter->GetOutput();
    typename ImageType::RegionType paddedImageRegion = paddedImage->GetLargestPossibleRegion();
    typename ImageType::PointType origin;
    paddedImage->TransformIndexToPhysicalPoint(paddedImageRegion.GetIndex(), origin);
    paddedImage->SetOrigin(origin);

    // get the results and cast them back to mitk. return via out parameter.
    outImage->InitializeByItk(paddedImage.GetPointer());
    CastToMitkImage(paddedImage, outImage);
}

SEXP addNeighborhoodToImageHelper(
  SEXP r_antsimage,
  SEXP r_center,
  SEXP r_rad,
  SEXP r_vec)
{
  typedef typename ImageType::Pointer  ImagePointerType;
  const unsigned int ImageDimension = ImageType::ImageDimension;
  typedef float                        PixelType;
  typename ImageType::Pointer image =
    Rcpp::as< ImagePointerType >( r_antsimage );
  Rcpp::NumericVector center( r_center );
  Rcpp::NumericVector rad( r_rad );
  Rcpp::NumericVector intvec( r_vec );
  if ( center.size() != ImageDimension )
    Rcpp::stop("addNeighborhoodToImageHelper dim error.");
  typename itk::NeighborhoodIterator<ImageType>::SizeType nSize;
  typename ImageType::IndexType ind;
  ind.Fill( 0 );
  for ( unsigned int i=0; i<ImageDimension; i++ )
    {
    nSize[i] = rad[i];
    ind[i] = center[i]; // R coords to ITK
    }
  itk::NeighborhoodIterator<ImageType> nit( nSize, image,
    image->GetLargestPossibleRegion() ) ;
// for each location in nitSearch, compute the correlation
// of the intvec with the nit neighborhood
  nit.SetLocation( ind );
  for( unsigned int i = 0; i < nit.Size(); i++ )
    {
    typename ImageType::IndexType ind2 = nit.GetIndex(i);
    PixelType lval = image->GetPixel( ind2 );
    image->SetPixel( ind2, lval + intvec[i] );
    }
  return 0;
}

SEXP eigenanatomyCppHelper(
  NumericMatrix X,
  SEXP r_mask,
  RealType sparseness,
  IntType nvecs,
  IntType its,
  IntType cthresh,
  RealType z,
  RealType smooth,
//  NumericMatrix initializationMatrix,
  Rcpp::List initializationList,
  IntType covering,
  RealType ell1,
  IntType verbose,
  IntType powerit,
  RealType priorWeight )
{
  enum { Dimension = ImageType::ImageDimension };
  typename ImageType::RegionType region;
  typedef typename ImageType::PixelType PixelType;
  typedef typename ImageType::Pointer ImagePointerType;
  typedef double                                        Scalar;
  typedef itk::ants::antsSCCANObject<ImageType, Scalar> SCCANType;
  typedef typename SCCANType::MatrixType                vMatrix;
  typename SCCANType::Pointer sccanobj = SCCANType::New();

  typename ImageType::Pointer mask = Rcpp::as<ImagePointerType>( r_mask );
  bool maskisnull = mask.IsNull();
// deal with the initializationList, if any
  unsigned int nImages = initializationList.size();
  if ( ( nImages > 0 ) && ( !maskisnull ) )
    {
    itk::ImageRegionIteratorWithIndex<ImageType> it( mask,
      mask->GetLargestPossibleRegion() );
    vMatrix priorROIMat( nImages , X.cols() );
    priorROIMat.fill( 0 );
    for ( unsigned int i = 0; i < nImages; i++ )
      {
      typename ImageType::Pointer init =
        Rcpp::as<ImagePointerType>( initializationList[i] );
      unsigned long ct = 0;
      it.GoToBegin();
      while ( !it.IsAtEnd() )
        {
        PixelType pix = it.Get();
        if ( pix >= 0.5 )
          {
          pix = init->GetPixel( it.GetIndex() );
          priorROIMat( i, ct ) = pix;
          ct++;
          }
        ++it;
        }
      }
    sccanobj->SetMatrixPriorROI( priorROIMat );
    nvecs = nImages;
    }
  sccanobj->SetPriorWeight( priorWeight );
  sccanobj->SetLambda( priorWeight );
// cast hack from Rcpp type to sccan type
  std::vector<double> xdat =
      Rcpp::as< std::vector<double> >( X );
  const double* _xdata = &xdat[0];
  vMatrix vnlX( _xdata , X.cols(), X.rows()  );
  vnlX = vnlX.transpose();

  sccanobj->SetGetSmall( false  );
  vMatrix priorROIMat;

//    sccanobj->SetMatrixPriorROI( priorROIMat);
//    sccanobj->SetMatrixPriorROI2( priorROIMat );
  sccanobj->SetCovering( covering );
  sccanobj->SetSilent(  ! verbose  );
  if( ell1 > 0 )
    {
    sccanobj->SetUseL1( true );
    }
  else
    {
    sccanobj->SetUseL1( false );
    }
  sccanobj->SetGradStep( vnl_math_abs( ell1 ) );
  sccanobj->SetMaximumNumberOfIterations( its );
  sccanobj->SetRowSparseness( z );
  sccanobj->SetSmoother( smooth );
  if ( sparseness < 0 ) sccanobj->SetKeepPositiveP(false);
  sccanobj->SetSCCANFormulation(  SCCANType::PQ );
  sccanobj->SetFractionNonZeroP( fabs( sparseness ) );
  sccanobj->SetMinClusterSizeP( cthresh );
  sccanobj->SetMatrixP( vnlX );
//  sccanobj->SetMatrixR( r ); // FIXME
  sccanobj->SetMaskImageP( mask );
  RealType truecorr = 0;
  if( powerit == 1 )
    {
    truecorr = sccanobj->SparseReconHome( nvecs );
    }
  else if ( priorWeight > 1.e-12 )
    truecorr = sccanobj->SparseReconPrior( nvecs, true );
  else truecorr = sccanobj->SparseRecon(nvecs);
//.........这里部分代码省略.........

SEXP sccanCppHelper(
  NumericMatrix X,
  NumericMatrix Y,
  SEXP r_maskx,
  SEXP r_masky,
  RealType sparsenessx,
  RealType sparsenessy,
  IntType nvecs,
  IntType its,
  IntType cthreshx,
  IntType cthreshy,
  RealType z,
  RealType smooth,
  Rcpp::List initializationListx,
  Rcpp::List initializationListy,
  IntType covering,
  RealType ell1,
  IntType verbose,
  RealType priorWeight )
{
  enum { Dimension = ImageType::ImageDimension };
  typename ImageType::RegionType region;
  typedef typename ImageType::PixelType PixelType;
  typedef typename ImageType::Pointer ImagePointerType;
  typedef double                                        Scalar;
  typedef itk::ants::antsSCCANObject<ImageType, Scalar> SCCANType;
  typedef typename SCCANType::MatrixType                vMatrix;
  typename SCCANType::Pointer sccanobj = SCCANType::New();

  typename ImageType::Pointer maskx = Rcpp::as<ImagePointerType>( r_maskx );
  typename ImageType::Pointer masky = Rcpp::as<ImagePointerType>( r_masky );

  bool maskxisnull = maskx.IsNull();
  bool maskyisnull = masky.IsNull();
// deal with the initializationList, if any
  unsigned int nImagesx = initializationListx.size();
  if ( ( nImagesx > 0 ) && ( !maskxisnull ) )
    {
    itk::ImageRegionIteratorWithIndex<ImageType> it( maskx,
      maskx->GetLargestPossibleRegion() );
    vMatrix priorROIMatx( nImagesx , X.cols() );
    priorROIMatx.fill( 0 );
    for ( unsigned int i = 0; i < nImagesx; i++ )
      {
      typename ImageType::Pointer init =
        Rcpp::as<ImagePointerType>( initializationListx[i] );
      unsigned long ct = 0;
      it.GoToBegin();
      while ( !it.IsAtEnd() )
        {
        PixelType pix = it.Get();
        if ( pix >= 0.5 )
          {
          pix = init->GetPixel( it.GetIndex() );
          priorROIMatx( i, ct ) = pix;
          ct++;
          }
        ++it;
        }
      }
    sccanobj->SetMatrixPriorROI( priorROIMatx );
    nvecs = nImagesx;
    }
  unsigned int nImagesy = initializationListy.size();
  if ( ( nImagesy > 0 ) && ( !maskyisnull ) )
    {
    itk::ImageRegionIteratorWithIndex<ImageType> it( masky,
      masky->GetLargestPossibleRegion() );
    vMatrix priorROIMaty( nImagesy , Y.cols() );
    priorROIMaty.fill( 0 );
    for ( unsigned int i = 0; i < nImagesy; i++ )
      {
      typename ImageType::Pointer init =
        Rcpp::as<ImagePointerType>( initializationListy[i] );
      unsigned long ct = 0;
      it.GoToBegin();
      while ( !it.IsAtEnd() )
        {
        PixelType pix = it.Get();
        if ( pix >= 0.5 )
          {
          pix = init->GetPixel( it.GetIndex() );
          priorROIMaty( i, ct ) = pix;
          ct++;
          }
        ++it;
        }
      }
    sccanobj->SetMatrixPriorROI2( priorROIMaty );
    nvecs = nImagesy;
    }

  sccanobj->SetPriorWeight( priorWeight );
  sccanobj->SetLambda( priorWeight );
// cast hack from Rcpp type to sccan type
  std::vector<double> xdat =
      Rcpp::as< std::vector<double> >( X );
  const double* _xdata = &xdat[0];
  vMatrix vnlX( _xdata , X.cols(), X.rows()  );
  vnlX = vnlX.transpose();
//.........这里部分代码省略.........

void mitk::MRNormLinearStatisticBasedFilter::InternalComputeMask(itk::Image<TPixel, VImageDimension>* itkImage)
{
  // Define all necessary Types
  typedef itk::Image<TPixel, VImageDimension> ImageType;
  typedef itk::Image<int, VImageDimension> MaskType;
  typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
  typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;

  typename MaskType::Pointer itkMask0 = MaskType::New();
  mitk::CastToItkImage(this->GetMask(), itkMask0);

  typename ImageType::Pointer outImage = ImageType::New();
  mitk::CastToItkImage(this->GetOutput(0), outImage);

  typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
  minMaxComputer->SetImage(itkImage);
  minMaxComputer->Compute();

  typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
  labelStatisticsImageFilter->SetUseHistograms(true);
  labelStatisticsImageFilter->SetHistogramParameters(256, minMaxComputer->GetMinimum(),minMaxComputer->GetMaximum());
  labelStatisticsImageFilter->SetInput( itkImage );

  labelStatisticsImageFilter->SetLabelInput(itkMask0);
  labelStatisticsImageFilter->Update();
  double median0 = labelStatisticsImageFilter->GetMedian(1);
  double mean0 = labelStatisticsImageFilter->GetMean(1);
  double stddev = labelStatisticsImageFilter->GetSigma(1);
  double modulo0=0;
  {
    auto histo = labelStatisticsImageFilter->GetHistogram(1);
    double maxFrequency=0;
    for (auto hIter=histo->Begin();hIter!=histo->End();++hIter)
    {
      if (maxFrequency < hIter.GetFrequency())
      {
        maxFrequency = hIter.GetFrequency();
        modulo0 = (histo->GetBinMin(0,hIter.GetInstanceIdentifier()) + histo->GetBinMax(0,hIter.GetInstanceIdentifier())) / 2.0;
      }
    }
  }

  double value0=0;
  switch (m_CenterMode)
  {
  case MRNormLinearStatisticBasedFilter::MEAN:
    value0=mean0; break;
  case MRNormLinearStatisticBasedFilter::MEDIAN:
    value0=median0; break;
  case MRNormLinearStatisticBasedFilter::MODE:
    value0=modulo0; break;
  }

  double offset = value0;
  double scaling = stddev;
  if (scaling < 0.0001)
    return;

  itk::ImageRegionIterator<ImageType> inIter(itkImage, itkImage->GetLargestPossibleRegion());
  itk::ImageRegionIterator<ImageType> outIter(outImage, outImage->GetLargestPossibleRegion());
  while (! inIter.IsAtEnd())
  {
    TPixel value = inIter.Value();
    outIter.Set((value - offset) / scaling);
    ++inIter;
    ++outIter;
  }
}

void mitk::SurfaceStampImageFilter::SurfaceStampProcessing(itk::Image<TPixel, 3> *input, MeshType *mesh)
{
  typedef itk::Image<TPixel, 3> ImageType;
  typedef itk::Image<unsigned char, 3> BinaryImageType;

  typedef itk::TriangleMeshToBinaryImageFilter<mitk::SurfaceStampImageFilter::MeshType, BinaryImageType> FilterType;

  BinaryImageType::Pointer binaryInput = BinaryImageType::New();
  binaryInput->SetSpacing(input->GetSpacing());
  binaryInput->SetOrigin(input->GetOrigin());
  binaryInput->SetDirection(input->GetDirection());
  binaryInput->SetRegions(input->GetLargestPossibleRegion());
  binaryInput->Allocate();
  binaryInput->FillBuffer(0);

  FilterType::Pointer filter = FilterType::New();
  filter->SetInput(mesh);
  filter->SetInfoImage(binaryInput);
  filter->SetInsideValue(1);
  filter->SetOutsideValue(0);
  filter->Update();

  BinaryImageType::Pointer resultImage = filter->GetOutput();
  resultImage->DisconnectPipeline();

  mitk::Image::Pointer outputImage = this->GetOutput();
  typename ImageType::Pointer itkOutputImage;
  mitk::CastToItkImage(outputImage, itkOutputImage);

  typedef itk::ImageRegionConstIterator<BinaryImageType> BinaryIteratorType;
  typedef itk::ImageRegionConstIterator<ImageType> InputIteratorType;
  typedef itk::ImageRegionIterator<ImageType> OutputIteratorType;

  BinaryIteratorType sourceIter(resultImage, resultImage->GetLargestPossibleRegion());
  sourceIter.GoToBegin();

  InputIteratorType inputIter(input, input->GetLargestPossibleRegion());
  inputIter.GoToBegin();

  OutputIteratorType outputIter(itkOutputImage, itkOutputImage->GetLargestPossibleRegion());
  outputIter.GoToBegin();

  typename ImageType::PixelType inputValue;
  unsigned char sourceValue;

  auto fgValue = static_cast<typename ImageType::PixelType>(m_ForegroundValue);
  auto bgValue = static_cast<typename ImageType::PixelType>(m_BackgroundValue);

  while (!sourceIter.IsAtEnd())
  {
    sourceValue = static_cast<unsigned char>(sourceIter.Get());
    inputValue = static_cast<typename ImageType::PixelType>(inputIter.Get());

    if (sourceValue != 0)
      outputIter.Set(fgValue);
    else if (m_OverwriteBackground)
      outputIter.Set(bgValue);
    else
      outputIter.Set(inputValue);

    ++sourceIter;
    ++inputIter;
    ++outputIter;
  }
}

SEXP jointLabelFusionNeighborhoodSearchHelper(
  SEXP r_intvec,
  SEXP r_center,
  unsigned int rad,
  unsigned int radSearch,
  SEXP r_antsimage,
  SEXP r_antsimageseg)
{
  unsigned int segval = 0;
  typedef typename ImageType::Pointer  ImagePointerType;
  const unsigned int ImageDimension = ImageType::ImageDimension;
  typedef float                        PixelType;
  typename ImageType::Pointer image =
    Rcpp::as< ImagePointerType >( r_antsimage );
  typename ImageType::Pointer imageseg =
      Rcpp::as< ImagePointerType >( r_antsimageseg );
  Rcpp::NumericVector intvec( r_intvec );
  Rcpp::NumericVector outvec =
    Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
  Rcpp::NumericVector bestvec =
    Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
  Rcpp::NumericVector outsegvec =
    Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
  Rcpp::NumericVector bestsegvec =
    Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
  Rcpp::NumericVector center( r_center );
  if ( center.size() != ImageDimension )
    Rcpp::stop("jointLabelFusionNeighborhoodSearchHelper dim error.");
  typename itk::NeighborhoodIterator<ImageType>::SizeType nSize;
  typename itk::NeighborhoodIterator<ImageType>::SizeType nSizeSearch;
  typename ImageType::IndexType ind;
  ind.Fill( 0 );
  for ( unsigned int i=0; i<ImageDimension; i++ )
    {
    nSize[i] = rad;
    nSizeSearch[i] = radSearch;
    ind[i] = center[i]; // R coords to ITK
    }
  itk::NeighborhoodIterator<ImageType> nit( nSize, image,
    image->GetLargestPossibleRegion() ) ;
  itk::NeighborhoodIterator<ImageType> nitSearch( nSizeSearch, image,
    image->GetLargestPossibleRegion() ) ;
// for each location in nitSearch, compute the correlation
// of the intvec with the nit neighborhood
  nitSearch.SetLocation( ind );
  PixelType bestcor = 1.e11;
  PixelType bestsd = 0;
  PixelType bestmean = 0;
  for( unsigned int i = 0; i < nitSearch.Size(); i++ )
    {
    typename ImageType::IndexType ind2 = nitSearch.GetIndex(i);
    nit.SetLocation( ind2 );
    PixelType outmean = 0;
    PixelType outsd = 0;
    PixelType inmean = 0;
    PixelType insd = 0;
    for ( unsigned int i=0; i < intvec.size(); i++ ) {
      PixelType pix = image->GetPixel( nit.GetIndex(i) );
      outvec[i] = pix;
      outsegvec[i] = imageseg->GetPixel( nit.GetIndex(i) );
      outmean += pix;
      inmean += intvec[i];
      }
    outmean /= ( static_cast<PixelType>(intvec.size()) );
    inmean /= ( static_cast<PixelType>(intvec.size()) );
    for ( unsigned int i=0; i < intvec.size(); i++ ) {
      // should use recursive formula in above loop
      outsd += ( outvec[i] - outmean ) * ( outvec[i] - outmean );
      insd += ( intvec[i] - inmean ) * ( intvec[i] - inmean );
      }
    outsd = sqrt(  outsd  );
    insd = sqrt(  insd  );
    PixelType sum_uv = 0;
    PixelType sum_psearch = 0;
    PixelType ssq_psearch = 0;
    unsigned int n = intvec.size();
    for(unsigned int i = 0; i < n; i++)
      {
      PixelType v = intvec[i];
      PixelType u = outvec[i];
      sum_psearch += u;
      ssq_psearch += u * u;
      sum_uv += u * v;
      }
    PixelType var_u_unnorm = ssq_psearch - sum_psearch * sum_psearch / n;
    if(var_u_unnorm < 1.0e-6)
      var_u_unnorm = 1.0e-6;
    PixelType locor = 0;
    if ( sum_uv > 0 )
      locor = ( -1.0 * (sum_uv * sum_uv) / var_u_unnorm );
    else
      locor = ( sum_uv * sum_uv ) / var_u_unnorm;
//  - (\Sum u_i v_i)^2 / z,   where z = sigma_v^2 * (n-1)
//      locor = locor / ( insd * outsd );
      if ( locor < bestcor )
        {
        segval = imageseg->GetPixel( ind2 );
        for ( unsigned int i=0; i < intvec.size(); i++ ) {
          bestvec[i] = outvec[i];
          bestsegvec[i] = outsegvec[i];
//.........这里部分代码省略.........

template <class inputType, unsigned int Dimension> medAbstractJob::medJobExitStatus medItkBiasCorrectionProcess::N4BiasCorrectionCore()
{
    medJobExitStatus eRes = medAbstractJob::MED_JOB_EXIT_SUCCESS;

    typedef itk::Image<inputType, Dimension > ImageType;
    typedef itk::Image <float, Dimension> OutputImageType;
    typedef itk::Image<unsigned char, Dimension> MaskImageType;
    typedef itk::N4BiasFieldCorrectionImageFilter<OutputImageType, MaskImageType, OutputImageType> BiasFilter;
    typedef itk::ConstantPadImageFilter<OutputImageType, OutputImageType> PadderType;
    typedef itk::ConstantPadImageFilter<MaskImageType, MaskImageType> MaskPadderType;
    typedef itk::ShrinkImageFilter<OutputImageType, OutputImageType> ShrinkerType;
    typedef itk::ShrinkImageFilter<MaskImageType, MaskImageType> MaskShrinkerType;
    typedef itk::BSplineControlPointImageFilter<typename BiasFilter::BiasFieldControlPointLatticeType, typename BiasFilter::ScalarImageType> BSplinerType;
    typedef itk::ExpImageFilter<OutputImageType, OutputImageType> ExpFilterType;
    typedef itk::DivideImageFilter<OutputImageType, OutputImageType, OutputImageType> DividerType;
    typedef itk::ExtractImageFilter<OutputImageType, OutputImageType> CropperType;

    unsigned int uiThreadNb = static_cast<unsigned int>(m_poUIThreadNb->value());
    unsigned int uiShrinkFactors = static_cast<unsigned int>(m_poUIShrinkFactors->value());
    unsigned int uiSplineOrder = static_cast<unsigned int>(m_poUISplineOrder->value());
    float fWienerFilterNoise = static_cast<float>(m_poFWienerFilterNoise->value());
    float fbfFWHM = static_cast<float>(m_poFbfFWHM->value());
    float fConvergenceThreshold = static_cast<float>(m_poFConvergenceThreshold->value());
    float fSplineDistance = static_cast<float>(m_poFSplineDistance->value());

    float fProgression = 0;

    QStringList oListValue = m_poSMaxIterations->value().split("x");

    std::vector<unsigned int> oMaxNumbersIterationsVector(oListValue.size());
    std::vector<float> oInitialMeshResolutionVect(Dimension);
    for (int i=0; i<oMaxNumbersIterationsVector.size(); ++i)
    {
       oMaxNumbersIterationsVector[i] = (unsigned int)oListValue[i].toInt();
    }
    oInitialMeshResolutionVect[0] = static_cast<float>(m_poFInitialMeshResolutionVect1->value());
    oInitialMeshResolutionVect[1] = static_cast<float>(m_poFInitialMeshResolutionVect2->value());
    oInitialMeshResolutionVect[2] = static_cast<float>(m_poFInitialMeshResolutionVect3->value());

    typename ImageType::Pointer image = dynamic_cast<ImageType *>((itk::Object*)(this->input()->data()));
    typedef itk::CastImageFilter <ImageType, OutputImageType> CastFilterType;
    typename CastFilterType::Pointer castFilter = CastFilterType::New();
    castFilter->SetInput(image);

    /********************************************************************************/
    /***************************** PREPARING STARTING *******************************/
    /********************************************************************************/

    /*** 0 ******************* Create filter and accessories ******************/
    ABORT_CHECKING(m_bAborting);
    typename BiasFilter::Pointer filter = BiasFilter::New();
    typename BiasFilter::ArrayType oNumberOfControlPointsArray;
    m_filter = filter;

    /*** 1 ******************* Read input image *******************************/
    ABORT_CHECKING(m_bAborting);
    fProgression = 1;
    updateProgression(fProgression);

    /*** 2 ******************* Creating Otsu mask *****************************/
    ABORT_CHECKING(m_bAborting);
    itk::TimeProbe timer;
    timer.Start();
    typename MaskImageType::Pointer maskImage = ITK_NULLPTR;
    typedef itk::OtsuThresholdImageFilter<OutputImageType, MaskImageType> ThresholderType;
    typename ThresholderType::Pointer otsu = ThresholderType::New();
    m_filter = otsu;
    otsu->SetInput(castFilter->GetOutput());
    otsu->SetNumberOfHistogramBins(200);
    otsu->SetInsideValue(0);
    otsu->SetOutsideValue(1);

    otsu->SetNumberOfThreads(uiThreadNb);
    otsu->Update();
    updateProgression(fProgression);
    maskImage = otsu->GetOutput();


    /*** 3A *************** Set Maximum number of Iterations for the filter ***/
    ABORT_CHECKING(m_bAborting);
    typename BiasFilter::VariableSizeArrayType itkTabMaximumIterations;
    itkTabMaximumIterations.SetSize(oMaxNumbersIterationsVector.size());
    for (int i = 0; i < oMaxNumbersIterationsVector.size(); ++i)
    {
        itkTabMaximumIterations[i] = oMaxNumbersIterationsVector[i];
    }
    filter->SetMaximumNumberOfIterations(itkTabMaximumIterations);

    /*** 3B *************** Set Fitting Levels for the filter *****************/
    typename BiasFilter::ArrayType oFittingLevelsTab;
    oFittingLevelsTab.Fill(oMaxNumbersIterationsVector.size());
    filter->SetNumberOfFittingLevels(oFittingLevelsTab);

    updateProgression(fProgression);

    /*** 4 ******************* Save image's index, size, origine **************/
    ABORT_CHECKING(m_bAborting);
    typename ImageType::IndexType oImageIndex = image->GetLargestPossibleRegion().GetIndex();
    typename ImageType::SizeType oImageSize = image->GetLargestPossibleRegion().GetSize();
    typename ImageType::PointType newOrigin = image->GetOrigin();
//.........这里部分代码省略.........