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itkImageGaussianModelEstimator.txx
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itkImageGaussianModelEstimator.txx
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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkImageGaussianModelEstimator.txx
Language: C++
Date: $Date$
Version: $Revision$
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 _itkImageGaussianModelEstimator_txx
#define _itkImageGaussianModelEstimator_txx
#include "itkImageGaussianModelEstimator.h"
namespace itk
{
template<class TInputImage,
class TMembershipFunction,
class TTrainingImage>
ImageGaussianModelEstimator<TInputImage, TMembershipFunction, TTrainingImage>
::ImageGaussianModelEstimator(void):
m_Covariance( NULL )
{
}
template<class TInputImage,
class TMembershipFunction,
class TTrainingImage>
ImageGaussianModelEstimator<TInputImage, TMembershipFunction, TTrainingImage>
::~ImageGaussianModelEstimator(void)
{
if ( m_Covariance ) delete [] m_Covariance;
}
/*
* PrintSelf
*/
template<class TInputImage,
class TMembershipFunction,
class TTrainingImage>
void
ImageGaussianModelEstimator<TInputImage, TMembershipFunction, TTrainingImage>
::PrintSelf( std::ostream& os, Indent indent ) const
{
os << indent << " " << std::endl;
os << indent << "Gaussian Models generated from the training data." << std::endl;
os << indent << "TrainingImage: " ;
os << m_TrainingImage.GetPointer() << std::endl;
os << indent << "Results printed in the superclass " << std::endl;
os << indent << " " << std::endl;
Superclass::PrintSelf(os,indent);
}// end PrintSelf
/**
* Generate data (start the model building process)
*/
template<class TInputImage,
class TMembershipFunction,
class TTrainingImage>
void
ImageGaussianModelEstimator<TInputImage, TMembershipFunction, TTrainingImage>
::GenerateData( )
{
this->EstimateModels();
}// end Generate data
// Takes a set of training images and returns the means
// and variance of the various classes defined in the
// training set.
template<class TInputImage,
class TMembershipFunction,
class TTrainingImage>
void
ImageGaussianModelEstimator<TInputImage, TMembershipFunction, TTrainingImage>
::EstimateModels()
{
//Do some error checking
InputImagePointer inputImage = this->GetInputImage();
// Check if the training and input image dimensions are same
if( (int)(TInputImage::ImageDimension) != (int)(TTrainingImage::ImageDimension) )
{
throw ExceptionObject(__FILE__, __LINE__);
}
InputImageSizeType
inputImageSize = inputImage->GetBufferedRegion().GetSize();
typedef InputImageSizeType TrainingImageSizeType;
TrainingImagePointer trainingImage = this->GetTrainingImage();
TrainingImageSizeType
trainingImageSize = trainingImage->GetBufferedRegion().GetSize();
// Check if size of the two inputs are same
for( unsigned int i = 0; i < TInputImage::ImageDimension; i++)
{
if( inputImageSize[i] != trainingImageSize[i] ) throw ExceptionObject(__FILE__, __LINE__);
}
//-------------------------------------------------------------------
// Set up the gaussian membership calculators
//-------------------------------------------------------------------
unsigned int numberOfModels = this->GetNumberOfModels();
//-------------------------------------------------------------------
// Call local function to estimate mean variances of the various
// class labels in the training set
// The statistics class functions have not been used since all the
// class statistics are calculated simultaneously here.
//-------------------------------------------------------------------
this->EstimateGaussianModelParameters();
//-------------------------------------------------------------------
// Populate the membership functions for all the classes
//-------------------------------------------------------------------
MembershipFunctionPointer membershipFunction;
for (unsigned int classIndex = 0 ; classIndex < numberOfModels ; classIndex++)
{
membershipFunction = TMembershipFunction::New() ;
membershipFunction->
SetNumberOfSamples( m_NumberOfSamples(classIndex, 0) ) ;
membershipFunction->
SetMean(m_Means.get_row( classIndex) ) ;
membershipFunction->
SetCovariance( m_Covariance[classIndex] ) ;
this->AddMembershipFunction( membershipFunction );
}
}// end train classifier
template<class TInputImage,
class TMembershipFunction,
class TTrainingImage>
void
ImageGaussianModelEstimator<TInputImage, TMembershipFunction, TTrainingImage>
::EstimateGaussianModelParameters()
{
// Set the iterators and the pixel type definition for the input image
InputImagePointer inputImage = this->GetInputImage();
InputImageIterator inIt( inputImage, inputImage->GetBufferedRegion() );
//-------------------------------------------------------------------
//-------------------------------------------------------------------
// Set the iterators and the pixel type definition for the training image
TrainingImagePointer trainingImage = this->GetTrainingImage();
TrainingImageIterator
trainingImageIt( trainingImage, trainingImage->GetBufferedRegion() );
//-------------------------------------------------------------------
unsigned int numberOfModels = (this->GetNumberOfModels());
//-------------------------------------------------------------------
// Set up the matrices to hold the means and the covariance for the
// training data
m_Means.set_size(numberOfModels, VectorDimension);
m_Means.fill(0);
m_NumberOfSamples.set_size(numberOfModels,1);
m_NumberOfSamples.fill(0);
// delete previous allocation first
if ( m_Covariance ) delete [] m_Covariance;
//Number of covariance matrices are equal to number of classes
m_Covariance = (MatrixType *) new MatrixType[numberOfModels];
for(unsigned int i = 0; i < numberOfModels; i++ )
{
m_Covariance[i].set_size( VectorDimension, VectorDimension );
m_Covariance[i].fill( 0 );
}
for( inIt.GoToBegin(); !inIt.IsAtEnd(); ++inIt, ++trainingImageIt )
{
unsigned int classIndex = (unsigned int) trainingImageIt.Get();
// Training data assumed =1 band; also the class indices go
// from 1, 2, ..., n while the corresponding memory goes from
// 0, 1, ..., n-1.
//Ensure that the training data is labelled appropriately
if( classIndex > numberOfModels )
{
throw ExceptionObject(__FILE__, __LINE__);
}
if(classIndex > 0)
{
m_NumberOfSamples[classIndex][0] +=1;
InputImagePixelType inImgVec = inIt.Get();
for(unsigned int band_x = 0; band_x < VectorDimension; band_x++)
{
m_Means[classIndex][band_x] += inImgVec[band_x];
for(unsigned int band_y = 0; band_y <= band_x; band_y++ )
{
m_Covariance[classIndex][band_x][band_y] += inImgVec[band_x] * inImgVec[band_y];
}
}
}
}// end for
//Loop through the classes to calculate the means and
for( unsigned int classIndex = 0; classIndex < numberOfModels; classIndex++ )
{
if( m_NumberOfSamples[classIndex][0] != 0 )
{
for( unsigned int i = 0; i < VectorDimension; i++ )
m_Means[classIndex][i] /= m_NumberOfSamples[classIndex][0];
}// end if
else
{
for( unsigned int i = 0; i < VectorDimension ; i++ )
m_Means[classIndex][i] = 0;
}// end else
if( ( m_NumberOfSamples[classIndex][0] - 1 ) != 0 )
{
for( unsigned int band_x = 0; band_x < VectorDimension; band_x++ )
{
for( unsigned int band_y=0; band_y <= band_x; band_y++ )
{
m_Covariance[classIndex][band_x][band_y]
/= (m_NumberOfSamples[classIndex][0]-1);
}// end for band_y loop
}// end for band_x loop
}// end if
else
{
for( unsigned int band_x = 0; band_x < VectorDimension; band_x++ )
for( unsigned int band_y = 0; band_y <= band_x; band_y++ )
m_Covariance[classIndex][band_x][band_y] = 0;
}// end else
MatrixType tempMeanSq;
tempMeanSq.set_size( VectorDimension, VectorDimension );
tempMeanSq.fill(0);
for( unsigned int band_x = 0; band_x < VectorDimension; band_x++)
{
for(unsigned int band_y=0; band_y<=band_x; band_y++)
{
tempMeanSq[band_x][band_y] =
m_Means[classIndex][band_x] * m_Means[classIndex][band_y];
}
}// end for band_x loop
if( ( m_NumberOfSamples[classIndex][0] - 1) != 0 )
{
tempMeanSq *= ( m_NumberOfSamples[classIndex][0]
/ (m_NumberOfSamples[classIndex][0] - 1 ) );
}
m_Covariance[classIndex] -= tempMeanSq;
// Fill the rest of the covairance matrix and make it symmetric
if(m_NumberOfSamples[classIndex][0] > 0)
{
unsigned int lastInX = (unsigned int)(VectorDimension - 1);
unsigned int upperY = (unsigned int)VectorDimension;
for(unsigned int band_x = 0; band_x < lastInX; band_x++)
{
for(unsigned int band_y=band_x+1; band_y < upperY; band_y++)
{
m_Covariance[classIndex][band_x][band_y]
= m_Covariance[classIndex][band_y][band_x];
}// end band_y loop
}// end band_x loop
}// end if loop
}// end class index loop
}// end EstimateGaussianModelParameters
} // namespace itk
#endif