libgofigure-dev 0.9.0-3+b1 / usr / include / gofigure2 / itkWatershedBasedCellSegmentation.txx

/*=========================================================================
 Authors: The GoFigure Dev. Team.
 at Megason Lab, Systems biology, Harvard Medical school, 2009-11

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#ifndef __itkWatershedBasedCellSegmentation_txx
#define __itkWatershedBasedCellSegmentation_txx

#include "itkWatershedBasedCellSegmentation.h"

namespace itk
{
template< class TFeatureImage, class TInputImage, class TSegmentImage >
WatershedBasedCellSegmentation< TFeatureImage, TInputImage, TSegmentImage >
::WatershedBasedCellSegmentation()
{
  m_NucleusRadius          = 4.0;
  m_CorrelationKernelSigma = 4.0;
  m_NucleusThresholdMin    = 10;
  m_NucleusThresholdMax    = 30;
  m_CorrelationThreshold1  = 0.50;
  m_MembraneThreshold      = 256;
  m_Alpha = 1.5;
  m_Beta = 3.0;

  this->Superclass::SetNumberOfRequiredInputs (1);
  this->Superclass::SetNumberOfRequiredOutputs (1);

  this->Superclass::SetNthOutput ( 0, TSegmentImage::New() );
}

template< class TFeatureImage, class TInputImage, class TSegmentImage >
void
WatershedBasedCellSegmentation< TFeatureImage, TInputImage, TSegmentImage >::GenerateData()
{
  PreprocessFilterPointer preprocess = PreprocessFilterType::New();

  preprocess->SetInput( this->GetInput() );
  preprocess->SetLargestCellRadius (m_NucleusRadius);
  preprocess->Update();

  FeatureImageConstPointer m_NucleiImg = preprocess->GetOutput();
  FeatureImageConstPointer m_MembraneImg = m_NucleiImg;

  SegmentImagePointer m_ForegroundImg;
    {
    // Apply Foreground filter
    ForegroundFilterPointer fgFilter = ForegroundFilterType::New();
    fgFilter->SetInput (0, m_NucleiImg);
    fgFilter->SetInput (1, m_MembraneImg);
    fgFilter->SetSigmaForm (m_CorrelationKernelSigma);   // in real coordinates
    fgFilter->SetThresholdCellmin (m_NucleusThresholdMin);
    fgFilter->SetThresholdCellmax (m_NucleusThresholdMax);
    fgFilter->SetThresholdMembrane (m_MembraneThreshold);
    fgFilter->SetThresholdForm (m_CorrelationThreshold1);
    fgFilter->SetLargestCellRadius (m_NucleusRadius);   // in real coordinates
    fgFilter->SetNumberOfThreads( this->GetNumberOfThreads() );
    fgFilter->Update();
   // std::cout << "Computed foreground" << std::endl;

    m_ForegroundImg = fgFilter->GetOutput();
    m_ForegroundImg->DisconnectPipeline();
    }

  // Gradient weighted distance -- Todo: extend with blob instead of laplace
  DistanceFilterPointer distFilter = DistanceFilterType::New();
  distFilter->SetInput (m_NucleiImg);
  distFilter->SetUseLevelSet(true);
  distFilter->SetForeground (m_ForegroundImg);
  distFilter->SetLargestCellRadius(m_NucleusRadius);
  distFilter->SetNucleiSigma (0.5);
  distFilter->SetAlpha(m_Alpha);
  distFilter->SetBeta(m_Beta);
  distFilter->Update();
  //std::cout << "Computed distance map" << std::endl;

  typename MinMaxCalculatorType::Pointer minMax = MinMaxCalculatorType::New();
  minMax->SetImage( distFilter->GetOutput() );
  minMax->ComputeMaximum();
  double max = static_cast< double >( minMax->GetMaximum() );

  RInvertPointer idistance = RInvertType::New();
  idistance->SetInput( distFilter->GetOutput() );
  idistance->SetMaximum(max);
  idistance->Update();
 // std::cout << "Inverted distance map" << std::endl;

  WatershedFilterPointer wshed = WatershedFilterType::New();
  wshed->SetInput( idistance->GetOutput() );
  wshed->SetMarkWatershedLine(false);
  wshed->SetLevel(1.0);
  wshed->FullyConnectedOn();
  wshed->SetNumberOfThreads( this->GetNumberOfThreads() );
  wshed->SetForegroundImage(m_ForegroundImg);
  wshed->Update();

  SegmentImagePointer output = wshed->GetOutput();
  output->DisconnectPipeline();

  SegmentImageSizeType  size = output->GetLargestPossibleRegion().GetSize();
  SegmentImageIndexType index, index2;
  SegmentImageSizeType  size2;
  for ( unsigned int i = 0; i < ImageDimension; i++ )
    {
    index[i] = static_cast< SegmentImageIndexValueType >(size[i] / 2);
    index2[i] = 1;
    size2[i] = size[i] - 2;
    }
  SegmentImagePixelType label = output->GetPixel(index);

  SegmentImageRegionType region;
  region.SetIndex(index2);
  region.SetSize(size2);

 // std::cout << "label: " << label << std::endl;

  SegmentIteratorType It( output, output->GetLargestPossibleRegion() );

  It.GoToBegin();
  if ( label > 0 )
    {
    while ( !It.IsAtEnd() )
      {
      index = It.GetIndex();

      if ( ( It.Get() ==  label ) && ( region.IsInside(index) ) )
        {
        It.Set(1);
        }
      else
        {
        It.Set(0);
        }

      ++It;
      }
    }
 // std::cout << "Computed watershed segmentation" << std::endl;

//   InputImagePointer smooth;
//   {
//     AntiAliasFilterPointer antialiaser = AntiAliasFilterType::New();
//     antialiaser->SetInput( output );//
//     antialiaser->SetMaximumRMSError( 1.0 );
//     antialiaser->SetNumberOfIterations( 100 );
//     antialiaser->SetInterpolateSurfaceLocation( true );
//     antialiaser->UseImageSpacingOn();
//     antialiaser->Update();
//     smooth = antialiaser->GetOutput();
//     smooth->DisconnectPipeline();
//   }
//
//   InputIteratorType iIt( smooth, smooth->GetLargestPossibleRegion() );
//   for( iIt.GoToBegin(), It.GoToBegin(); !It.IsAtEnd(); ++iIt, ++It )
//   {
//     if ( iIt.Get() > 0 )
//     {
//       It.Set( 1 );
//     }
//     else
//     {
//       It.Set( 0 );
//     }
//   }

  this->GraftOutput(output);
}

template< class TFeatureImage, class TInputImage, class TSegmentImage >
void
WatershedBasedCellSegmentation< TFeatureImage, TInputImage, TSegmentImage >::PrintSelf(std::ostream & os,
                                                                                       Indent indent) const
{
  (void)os;
  (void)indent;
}

} /* end namespace itk */

#endif