itkMRFImageFilter.txx

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/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkMRFImageFilter.txx,v $
  Language:  C++
  Date:      $Date: 2009-10-27 16:06:46 $
  Version:   $Revision: 1.69 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#ifndef __itkMRFImageFilter_txx
#define __itkMRFImageFilter_txx
#include "itkMRFImageFilter.h"

namespace itk
{

template<class TInputImage, class TClassifiedImage>
MRFImageFilter<TInputImage,TClassifiedImage>
::MRFImageFilter(void):
  m_NumberOfClasses(0),
  m_MaximumNumberOfIterations(50),
  m_ErrorCounter(0),
  m_NeighborhoodSize(27),
  m_TotalNumberOfValidPixelsInOutputImage(1),
  m_TotalNumberOfPixelsInInputImage(1),
  m_ErrorTolerance(0.2),
  m_SmoothingFactor(1),
  m_ClassProbability(0),
  m_NumberOfIterations(0),
  m_StopCondition(MaximumNumberOfIterations),
  m_ClassifierPtr(0)
{

  if( (int)InputImageDimension != (int)ClassifiedImageDimension )
    {
    OStringStream msg;
    msg << "Input image dimension: " << InputImageDimension << " != output image dimension: " << ClassifiedImageDimension; 
    throw ExceptionObject(__FILE__, __LINE__,msg.str().c_str(),ITK_LOCATION);
    }
  m_InputImageNeighborhoodRadius.Fill(0);
  m_MRFNeighborhoodWeight.resize(0);
  m_NeighborInfluence.resize(0);
  m_DummyVector.resize(0);
  this->SetMRFNeighborhoodWeight( m_DummyVector );
  this->SetDefaultMRFNeighborhoodWeight();
}

template<class TInputImage, class TClassifiedImage>
MRFImageFilter<TInputImage, TClassifiedImage>
::~MRFImageFilter(void)
{

}

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::PrintSelf( std::ostream& os, Indent indent ) const
{
  Superclass::PrintSelf(os,indent);
  unsigned int i;

  os << indent <<" MRF Image filter object " << std::endl;

  os << indent <<" Number of classes: " << m_NumberOfClasses << std::endl;

  os << indent <<" Maximum number of iterations: " << 
    m_MaximumNumberOfIterations << std::endl;

  os << indent <<" Error tolerance for convergence: " << 
    m_ErrorTolerance << std::endl;

  os << indent <<" Size of the MRF neighborhood radius:" << 
    m_InputImageNeighborhoodRadius << std::endl;

  os << indent <<" Number of elements in MRF neighborhood :" <<
    static_cast<unsigned long>( m_MRFNeighborhoodWeight.size() ) << std::endl;

  os << indent <<" Neighborhood weight : [";
  const unsigned int neighborhoodWeightSize = 
    static_cast<unsigned int>( m_MRFNeighborhoodWeight.size() );
  for (i=0; i+1 < neighborhoodWeightSize; i++)
    {
    os << m_MRFNeighborhoodWeight[i] << ", ";
    }
  os << m_MRFNeighborhoodWeight[i] << "]" << std::endl;

  os << indent <<" Smoothing factor for the MRF neighborhood:" << 
    m_SmoothingFactor << std::endl;

  os << indent << "StopCondition: "
     << m_StopCondition << std::endl;

  os << indent <<" Number of iterations: " << 
    m_NumberOfIterations << std::endl;

}// end PrintSelf

/*
 * GenerateInputRequestedRegion method.
 */
template <class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::GenerateInputRequestedRegion()
{

  // this filter requires the all of the input images 
  // to be at the size of the output requested region
  InputImagePointer inputPtr = 
    const_cast< InputImageType * >( this->GetInput() );
  OutputImagePointer outputPtr = this->GetOutput();
  if (inputPtr && outputPtr)
    {
    inputPtr->SetRequestedRegion( outputPtr->GetRequestedRegion() );
    }
}


template <class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::EnlargeOutputRequestedRegion(
  DataObject *output )
{

  // this filter requires the all of the output image to be in
  // the buffer
  TClassifiedImage *imgData;
  imgData = dynamic_cast<TClassifiedImage*>( output );
  imgData->SetRequestedRegionToLargestPossibleRegion();

}

template <class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::GenerateOutputInformation()
{
  typename TInputImage::ConstPointer input = this->GetInput();
  typename TClassifiedImage::Pointer output = this->GetOutput();
  output->SetLargestPossibleRegion( input->GetLargestPossibleRegion() );

}

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::GenerateData()
{
  //First run the Gaussian classifier calculator and
  //generate the Gaussian model for the different classes
  //and then generate the initial labelled dataset.

  InputImageConstPointer inputImage = this->GetInput();
    
  //Give the input image and training image set to the  
  // classifier
  m_ClassifierPtr->SetInputImage( inputImage );

  //Run the gaussian classifier algorithm
  m_ClassifierPtr->Update();

  //Allocate memory for the labelled images
  this->Allocate();

  this->ApplyMRFImageFilter();
  //Set the output labelled and allocate the memory
  LabelledImagePointer outputPtr = this->GetOutput();

  //Allocate the output buffer memory 
  outputPtr->SetBufferedRegion( outputPtr->GetRequestedRegion() );
  outputPtr->Allocate();

  //--------------------------------------------------------------------
  //Copy labelling result to the output buffer
  //--------------------------------------------------------------------
  // Set the iterators to the processed image
  //--------------------------------------------------------------------
  LabelledImageRegionIterator  
    labelledImageIt( m_ClassifierPtr->GetClassifiedImage(), 
                     outputPtr->GetRequestedRegion() );

  //--------------------------------------------------------------------
  // Set the iterators to the output image buffer
  //--------------------------------------------------------------------
  LabelledImageRegionIterator  
    outImageIt( outputPtr, outputPtr->GetRequestedRegion() );

  //--------------------------------------------------------------------

  while ( !outImageIt.IsAtEnd() )
    {
    LabelledImagePixelType labelvalue = 
      ( LabelledImagePixelType ) labelledImageIt.Get();

    outImageIt.Set( labelvalue );
    ++labelledImageIt;
    ++outImageIt;
    }// end while
        
}// end GenerateData

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::SetClassifier( typename ClassifierType::Pointer ptrToClassifier )
{
  if( ( ptrToClassifier.IsNull() ) || (m_NumberOfClasses <= 0) )
    {
    throw ExceptionObject(__FILE__, __LINE__,"NumberOfClasses is <= 0",ITK_LOCATION);
    }
  m_ClassifierPtr = ptrToClassifier;
  m_ClassifierPtr->SetNumberOfClasses( m_NumberOfClasses );
}//end SetPtrToClassifier

//-------------------------------------------------------
//Set the neighborhood radius
//-------------------------------------------------------

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::SetNeighborhoodRadius( const unsigned long radiusValue)
{
  //Set up the neighbor hood 
  NeighborhoodRadiusType radius;
  for(unsigned int i=0;i < InputImageDimension; ++i)
    {
    radius[i] = radiusValue;
    }   
  this->SetNeighborhoodRadius( radius ); 

}// end SetNeighborhoodRadius

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::SetNeighborhoodRadius( const unsigned long *radiusArray)
{
  NeighborhoodRadiusType  radius;
  for(unsigned int i=0;i < InputImageDimension; ++i)
    {
    radius[i] = radiusArray[i];
    }
  //Set up the neighbor hood 
  this->SetNeighborhoodRadius( radius ); 

}// end SetNeighborhoodRadius

//Set the neighborhood radius
template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::SetNeighborhoodRadius( const NeighborhoodRadiusType &radius)
{
  //Set up the neighbor hood 
  for(unsigned int i=0;i < InputImageDimension; ++i)
    {
    m_InputImageNeighborhoodRadius[ i ] =
      radius[ i ];
    m_LabelledImageNeighborhoodRadius[ i ] =
      radius[ i ];
    m_LabelStatusImageNeighborhoodRadius[ i ] =
      radius[ i ];
    }

}// end SetNeighborhoodRadius
//-------------------------------------------------------

//-------------------------------------------------------
//Set the neighborhood weights
//-------------------------------------------------------

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::SetDefaultMRFNeighborhoodWeight( )
{

  // Set the beta matrix of a 3x3x3 kernel
  // The index starts from 0 going along the three dimensions
  // in the order of [coloumn], [row], [depth].

  //Allocate memory for the weights of the 3D MRF algorithm
  // and corresponding memory offsets.

  //-----------------------------------------------------
  //Determine the default neighborhood size
  //-----------------------------------------------------   
  m_NeighborhoodSize = 1;
  int neighborhoodRadius = 1; //Default assumes a radius of 1 
  for( unsigned int i = 0; i < InputImageDimension; i++ )
    {
    m_NeighborhoodSize *= (2*neighborhoodRadius+1);
    }
  if( (InputImageDimension == 3) )
    {
    //Assumes a default 3x3x3 window size
    m_MRFNeighborhoodWeight.resize( m_NeighborhoodSize );

    for( int i = 0; i < 9; i++ ) 
        m_MRFNeighborhoodWeight[i] = 1.3 * m_SmoothingFactor;

    for( int i = 9; i < 18; i++ )
        m_MRFNeighborhoodWeight[i] = 1.7 * m_SmoothingFactor;

    for( int i = 18; i < 27; i++ )
        m_MRFNeighborhoodWeight[i] = 1.3 * m_SmoothingFactor;

    // Change the center weights
    m_MRFNeighborhoodWeight[4]  = 1.5 * m_SmoothingFactor;
    m_MRFNeighborhoodWeight[13] = 0.0;
    m_MRFNeighborhoodWeight[22] = 1.5 * m_SmoothingFactor;
    }
  if(  (InputImageDimension == 2) )
    {
    //Assumes a default 3x3x3 window size
    m_MRFNeighborhoodWeight.resize( m_NeighborhoodSize );

    for( int i = 0; i < m_NeighborhoodSize; i++ ) 
        m_MRFNeighborhoodWeight[i] = 1.7 * m_SmoothingFactor;

    // Change the center weights
    m_MRFNeighborhoodWeight[4]  = 0;
    } else
  if( (InputImageDimension > 3) )
    {
    for( int i = 0; i < m_NeighborhoodSize; i++ ) 
      {
      m_MRFNeighborhoodWeight[i] = 1;
      }
    }
}// SetMRFNeighborhoodWeight

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::SetMRFNeighborhoodWeight( std::vector<double> betaMatrix)
{
  if( betaMatrix.size() == 0 )
    {
    //Call a the function to set the default neighborhood
    this->SetDefaultMRFNeighborhoodWeight();
    }
  else
    {
    m_NeighborhoodSize = 1;
    for( unsigned int i = 0; i < InputImageDimension; i++ )
      {
      m_NeighborhoodSize *= (2*m_InputImageNeighborhoodRadius[i]+1);
      }

    if( m_NeighborhoodSize != static_cast<int>(betaMatrix.size()) )
      {
      throw ExceptionObject(__FILE__, __LINE__, "NeighborhoodSize != betaMatrix.szie()", ITK_LOCATION);
      }

    //Allocate memory for the weights of the 3D MRF algorithm
    // and corresponding memory offsets.
    m_MRFNeighborhoodWeight.resize( m_NeighborhoodSize );

    for( unsigned int i = 0; i < betaMatrix.size(); i++ ) 
      {
      m_MRFNeighborhoodWeight[i] = (betaMatrix[i] * m_SmoothingFactor);
      }
    }
}// end SetDefaultMRFNeighborhoodWeight


//-------------------------------------------------------
//-------------------------------------------------------
//Allocate algorithm specific resources
//-------------------------------------------------------
template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::Allocate()
{
  if( m_NumberOfClasses <= 0 )
    {
    throw ExceptionObject(__FILE__, __LINE__,"NumberOfClasses <= 0.",ITK_LOCATION);
    }

  InputImageSizeType inputImageSize = this->GetInput()->GetBufferedRegion().GetSize();
  
  //---------------------------------------------------------------------
  //Get the number of valid pixels in the output MRF image
  //---------------------------------------------------------------------
  int tmp;
  for( unsigned int i=0; i < InputImageDimension; i++ )
    {
    tmp = static_cast<int>(inputImageSize[i]);

    m_TotalNumberOfPixelsInInputImage *= tmp;
 
    m_TotalNumberOfValidPixelsInOutputImage *= 
      ( tmp - 2*m_InputImageNeighborhoodRadius[i] ); 
    }  

  //Allocate the label image status
  LabelStatusIndexType index;
  index.Fill(0);
 
  LabelStatusRegionType region;
  region.SetSize( inputImageSize );
  region.SetIndex( index );

  m_LabelStatusImage = LabelStatusImageType::New();
  m_LabelStatusImage->SetLargestPossibleRegion( region );
  m_LabelStatusImage->SetBufferedRegion( region );
  m_LabelStatusImage->Allocate();

  LabelStatusImageIterator rIter( m_LabelStatusImage, 
                                  m_LabelStatusImage->GetBufferedRegion() );

  //Initialize the label status image to 1
  while( !rIter.IsAtEnd() )
    {
    rIter.Set( 1 );
    ++rIter;
    }

}// Allocate
//-------------------------------------------------------
//-------------------------------------------------------
//Apply the MRF image filter
//-------------------------------------------------------

template<class TInputImage, class TClassifiedImage>
void 
MRFImageFilter<TInputImage, TClassifiedImage>
::ApplyMRFImageFilter()
{

  InputImageSizeType inputImageSize = 
    this->GetInput()->GetBufferedRegion().GetSize();

  int totalNumberOfPixelsInInputImage = 1;

  for( unsigned int i = 0; i < InputImageDimension; i++ )  
    {
    totalNumberOfPixelsInInputImage *= static_cast<int>(inputImageSize[ i ]);
    }

  int maxNumPixelError = Math::Round<int>( m_ErrorTolerance * 
                      m_TotalNumberOfValidPixelsInOutputImage);

  m_NumberOfIterations = 0;
  do
    {
    itkDebugMacro(<< "Iteration No." << m_NumberOfIterations);
 
    MinimizeFunctional();
    m_NumberOfIterations += 1;
    m_ErrorCounter = m_TotalNumberOfValidPixelsInOutputImage - 
      totalNumberOfPixelsInInputImage;  
 
    LabelStatusImageIterator rIter( m_LabelStatusImage, 
                                    m_LabelStatusImage->GetBufferedRegion() );

    //Initialize the label status image to 1
    while( !rIter.IsAtEnd() )
      {
      if ( rIter.Get( ) == 1 ) m_ErrorCounter += 1;
      ++rIter;
      }  
    }
  while(( m_NumberOfIterations < m_MaximumNumberOfIterations ) && 
        ( m_ErrorCounter > maxNumPixelError ) ); 

  //Determine stop condition
  if( m_NumberOfIterations >= m_MaximumNumberOfIterations )
    {
    m_StopCondition = MaximumNumberOfIterations;
    }
  else if( m_ErrorCounter <= maxNumPixelError ) 
    {
    m_StopCondition = ErrorTolerance;
    }
    
}// ApplyMRFImageFilter

//-------------------------------------------------------
//-------------------------------------------------------
//Minimize the functional
//-------------------------------------------------------

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::MinimizeFunctional()
{
  //This implementation uses the ICM algorithm
  ApplyICMLabeller();
}

//-------------------------------------------------------
//-------------------------------------------------------
//Core of the ICM algorithm
//-------------------------------------------------------
template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::ApplyICMLabeller()
{

  //---------------------------------------------------------------------
  // Loop through the data set and classify the data
  //---------------------------------------------------------------------
  m_NeighborInfluence.resize( m_NumberOfClasses ); 

  //Varible to store the input pixel vector value
  //InputImagePixelType inputPixel;

  m_MahalanobisDistance.resize( m_NumberOfClasses );   

  //---------------------------------------------------------------------
  // Set up the neighborhood iterators and the valid neighborhoods
  // for iteration
  //---------------------------------------------------------------------

  //Set up the nighborhood iterators
 
  //Define the face list for the input/labelled image
  InputImageFacesCalculator        inputImageFacesCalculator;
  LabelledImageFacesCalculator     labelledImageFacesCalculator;
  LabelStatusImageFacesCalculator  labelStatusImageFacesCalculator;

  InputImageFaceListType           inputImageFaceList;
  LabelledImageFaceListType        labelledImageFaceList;
  LabelStatusImageFaceListType     labelStatusImageFaceList;

  //Compute the faces for the neighborhoods in the input/labelled image 
  InputImageConstPointer inputImage = this->GetInput();
  inputImageFaceList =
    inputImageFacesCalculator( inputImage, 
                               inputImage->GetBufferedRegion(),
                               m_InputImageNeighborhoodRadius );

  LabelledImagePointer labelledImage = m_ClassifierPtr->GetClassifiedImage();
  labelledImageFaceList =
    labelledImageFacesCalculator( labelledImage, 
                                  labelledImage->GetBufferedRegion(),
                                  m_LabelledImageNeighborhoodRadius );  

  labelStatusImageFaceList =
    labelStatusImageFacesCalculator( m_LabelStatusImage, 
                                     m_LabelStatusImage->GetBufferedRegion(),
                                     m_LabelStatusImageNeighborhoodRadius );  
  //Set up a face list iterator
  InputImageFaceListIterator inputImageFaceListIter
    = inputImageFaceList.begin();

  LabelledImageFaceListIterator labelledImageFaceListIter
    = labelledImageFaceList.begin();

  LabelStatusImageFaceListIterator labelStatusImageFaceListIter
    = labelStatusImageFaceList.begin();

  //Walk through the entire data set (not visiting the boundaries )
  InputImageNeighborhoodIterator 
    nInputImageNeighborhoodIter( m_InputImageNeighborhoodRadius,
                                 inputImage, 
                                 *inputImageFaceListIter );
  
  LabelledImageNeighborhoodIterator
    nLabelledImageNeighborhoodIter( m_LabelledImageNeighborhoodRadius,
                                    labelledImage, 
                                    *labelledImageFaceListIter );

  LabelStatusImageNeighborhoodIterator
    nLabelStatusImageNeighborhoodIter( m_LabelStatusImageNeighborhoodRadius,
                                       m_LabelStatusImage, 
                                       *labelStatusImageFaceListIter );

  //---------------------------------------------------------------------
  while( !nInputImageNeighborhoodIter.IsAtEnd() )
    {

    //Process each neighborhood
    this->DoNeighborhoodOperation( nInputImageNeighborhoodIter,
                                   nLabelledImageNeighborhoodIter,
                                   nLabelStatusImageNeighborhoodIter );

                                   
    ++nInputImageNeighborhoodIter;
    ++nLabelledImageNeighborhoodIter;
    ++nLabelStatusImageNeighborhoodIter;

    } 
}//ApplyICMlabeller

//-------------------------------------------------------
//-------------------------------------------------------
//Function that performs the MRF operation with each neighborhood
//-------------------------------------------------------

template<class TInputImage, class TClassifiedImage>
void
MRFImageFilter<TInputImage, TClassifiedImage>
::DoNeighborhoodOperation( const InputImageNeighborhoodIterator &imageIter,
                           LabelledImageNeighborhoodIterator &labelledIter,
                           LabelStatusImageNeighborhoodIterator &labelStatusIter )
{

  unsigned int index;

  //Read the pixel of interest and get its corresponding membership value
  InputImagePixelType   *inputPixelVec = imageIter.GetCenterValue();

  const std::vector<double> & pixelMembershipValue = 
    m_ClassifierPtr->GetPixelMembershipValue( *inputPixelVec );


  //Reinitialize the neighborhood influence at the beginning of the 
  //neighborhood operation
  for( index = 0; index < m_NeighborInfluence.size(); index++ ) 
    {
    m_NeighborInfluence[index]= 0;
    }

  LabelledImagePixelType labelledPixel; 
  
  //Begin neighborhood processing. Calculate the prior for each label
  for( int i = 0; i < m_NeighborhoodSize; ++i )
    {

    labelledPixel = labelledIter.GetPixel( i );
    index = (unsigned int) labelledPixel;
    m_NeighborInfluence[ index ] += m_MRFNeighborhoodWeight[ i ];

    }//End neighborhood processing

  //Add the prior probability to the pixel probability
  for( index = 0; index < m_NumberOfClasses; index++ )
    {
    m_MahalanobisDistance[index] = m_NeighborInfluence[index] - 
      pixelMembershipValue[index];
    }

  //Determine the maximum possible distance
  double maximumDistance = -1e+20;
  int pixLabel = -1;
  double tmpPixDistance;
  for( index = 0; index < m_NumberOfClasses; index++ )
    {
    tmpPixDistance = m_MahalanobisDistance[index]; 
    if ( tmpPixDistance > maximumDistance )
      {
      maximumDistance = tmpPixDistance;
      pixLabel = index;
      }// if
    }// for

  //Read the current pixel label
  LabelledImagePixelType *previousLabel = labelledIter.GetCenterValue();

  //Check if the labelled pixel value in the previous iteration has changed
  //If the value has changed then update the m_LabelStatus set;

  if( pixLabel != (int) ( *previousLabel ) )
    {
    labelledIter.SetCenterPixel( pixLabel );
    for( int i = 0; i < m_NeighborhoodSize; ++i )
      {
      labelStatusIter.SetPixel( i, 1 );
      } //End neighborhood processing
    } //end if
  else
    {
    labelStatusIter.SetCenterPixel( 0 );
    }

}// end DoNeighborhoodOperation


} // namespace itk

#endif