itkVoronoiSegmentationImageFilter.txx

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

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkVoronoiSegmentationImageFilter.txx,v $
  Language:  C++
  Date:      $Date: 2009-01-27 19:30:17 $
  Version:   $Revision: 1.39 $

  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 __itkVoronoiSegmentationImageFilter_txx
#define __itkVoronoiSegmentationImageFilter_txx
#include "itkVoronoiSegmentationImageFilter.h"

#include "itkImageRegionIteratorWithIndex.h"
#include "itkImageRegionConstIteratorWithIndex.h"


namespace itk
{

/* constructor: seting the default value of the parameters */
template <class TInputImage, class TOutputImage, class TBinaryPriorImage>
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::VoronoiSegmentationImageFilter()
{
  m_MeanPercentError = 0.10;
  m_STDPercentError = 1.5;
  m_Mean = m_STD = m_MeanTolerance = m_STDTolerance = 0.0;
}

/* destructor */
template <class TInputImage, class TOutputImage, class TBinaryPriorImage>
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::~VoronoiSegmentationImageFilter()
{
}

template <class TInputImage, class TOutputImage, class TBinaryPriorImage>
void
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::SetMeanPercentError(double x)
{
  m_MeanPercentError = x;
  m_MeanTolerance = x*m_Mean;
}


template <class TInputImage, class TOutputImage, class TBinaryPriorImage>
void
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::SetSTDPercentError(double x)
{
  m_STDPercentError = x;
  m_STDTolerance = x*m_STD;
}

template <class TInputImage, class TOutputImage, class TBinaryPriorImage>
bool
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::TestHomogeneity(IndexList &Plist)
{
  int num = static_cast<int>( Plist.size() );
  int i;
  double getp;
  double addp=0;
  double addpp=0;
  for(i=0;i<num;i++)
    {
    getp = (double)(this->GetInput()->GetPixel(Plist[i]));
    addp=addp+getp;
    addpp=addpp+getp*getp;
    }

  double savemean,saveSTD;
  if(num > 1)
    {
    savemean = addp/num;
    saveSTD = vcl_sqrt((addpp - (addp*addp)/(num) )/(num-1));
    }
  else
    {
    savemean = 0;
    saveSTD = -1;
    }

  //   // jvm - Mahalanobis distance
  //   if (savevar > 0 && vcl_fabs(savemean - m_Mean) / m_Var < 2.5)
  //     return true;
  //   else
  //     return false;
  
  savemean -= m_Mean;
  saveSTD -= m_STD;
  if( (savemean>-m_MeanTolerance) && (savemean<m_MeanTolerance) 
      && (saveSTD<m_STDTolerance) )
    {
    return 1;
    }
  else
    {
    return 0;
    }
}

template <class TInputImage, class TOutputImage , class TBinaryPriorImage>
void
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::TakeAPrior(const BinaryObjectImage* aprior)
{
  RegionType region = this->GetInput()->GetRequestedRegion();
  itk::ImageRegionConstIteratorWithIndex <BinaryObjectImage> ait(aprior, region);
  itk::ImageRegionConstIteratorWithIndex <InputImageType> iit(this->GetInput(), region);

  this->m_Size = this->GetInput()->GetRequestedRegion().GetSize();

  int num=0;
  float addp=0;
  float addpp=0;
  float currp;

  unsigned int i,j;
  unsigned int minx = 0,miny = 0,maxx = 0,maxy = 0;
  bool status=0;
  for(i=0;i<this->m_Size[1];i++)
    {
    for(j=0;j<this->m_Size[0];j++)
      {
      if( (status==0)&&(ait.Get()) )
        {
        miny=i;
        minx=j;
        maxy=i;
        maxx=j;
        status=1;
        } 
      else if( (status==1)&&(ait.Get()) )
        {
        maxy=i;
        if(minx>j) minx=j;
        if(maxx<j) maxx=j;
        }  
      ++ait;
      }
    }
  
  float addb=0;
  float addbb=0;
  int numb=0;

  ait.GoToBegin();
  iit.GoToBegin();
  for(i=0;i<miny;i++)
    {
    for(j=0;j<this->m_Size[0];j++)
      {
      ++ait;
      ++iit;
      }
    }
  for(i=miny;i<=maxy;i++)
    {
    for(j=0;j<minx;j++)
      {
      ++ait;
      ++iit;
      }
    for(j=minx;j<=maxx;j++)
      {
      if(ait.Get())
        {
        num++;
        currp = (float)(iit.Get());
        addp += currp;
        addpp += currp*currp;
        }
      else
        {
        numb++;
        currp = (float)(iit.Get());
        addb += currp;
        addbb += currp*currp;
        }
      ++ait;++iit;
      }
    for(j=maxx+1;j<this->m_Size[0];j++)
      {
      ++ait;
      ++iit;
      }
    }

  m_Mean = addp/num;
  m_STD = vcl_sqrt((addpp - (addp*addp)/num)/(num-1));
  float b_Mean = addb/numb;

  if(this->GetUseBackgroundInAPrior())
    {
    m_MeanTolerance = vcl_fabs(m_Mean-b_Mean)*this->GetMeanDeviation();
    }
  else
    {
    m_MeanTolerance = m_Mean*m_MeanPercentError;
    }
  m_STDTolerance = m_STD*m_STDPercentError;
}

template <class TInputImage, class TOutputImage, class TBinaryPriorImage>
void
VoronoiSegmentationImageFilter <TInputImage,TOutputImage,TBinaryPriorImage>
::PrintSelf(std::ostream& os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "Mean = " << m_Mean << std::endl;
  os << indent << "MeanTolerance = " << m_MeanTolerance << std::endl;
  os << indent << "MeanPercentError = " << m_MeanPercentError << std::endl;
  os << indent << "STD = " << m_STD << std::endl;
  os << indent << "STDTolerance = " << m_STDTolerance << std::endl;
  os << indent << "STDPercentError = " << m_STDPercentError << std::endl;
}

} //end namespace

#endif