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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkShapePriorMAPCostFunction.txx,v $
Language: C++
Date: $Date: 2004-12-21 22:47:29 $
Version: $Revision: 1.7 $
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 __itkShapePriorMAPCostFunction_txx_
#define __itkShapePriorMAPCostFunction_txx_
#include "itkShapePriorMAPCostFunction.h"
namespace itk {
/**
* Constructor
*/
template <class TFeatureImage, class TOutputPixel>
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::ShapePriorMAPCostFunction()
{
m_GaussianFunction = GaussianKernelFunction::New();
m_ShapeParameterMeans = ArrayType( 1 );
m_ShapeParameterMeans.Fill( 0.0 );
m_ShapeParameterStandardDeviations = ArrayType( 1 );
m_ShapeParameterStandardDeviations.Fill( 0.0 );
m_Weights.Fill( 1.0 );
}
/**
* PrintSelf
*/
template <class TFeatureImage, class TOutputPixel>
void
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::PrintSelf( std::ostream& os, Indent indent) const
{
Superclass::PrintSelf( os, indent );
os << indent << "ShapeParameterMeans: " << m_ShapeParameterMeans << std::endl;
os << indent << "ShapeParameterStandardDeviations: ";
os << m_ShapeParameterStandardDeviations << std::endl;
os << indent << "Weights: " << m_Weights << std::endl;
}
/**
*
*/
template <class TFeatureImage, class TOutputPixel>
typename ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::MeasureType
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::ComputeLogInsideTerm( const ParametersType & parameters ) const
{
this->m_ShapeFunction->SetParameters( parameters );
typename NodeContainerType::ConstIterator iter = this->GetActiveRegion()->Begin();
typename NodeContainerType::ConstIterator end = this->GetActiveRegion()->End();
MeasureType counter = 0.0;
// count the number of pixels inside the current contour but outside the current shape
while( iter != end )
{
NodeType node = iter.Value();
typename ShapeFunctionType::PointType point;
this->GetFeatureImage()->TransformIndexToPhysicalPoint( node.GetIndex(), point );
if ( node.GetValue() <= 0.0 )
{
double value = this->m_ShapeFunction->Evaluate( point );
if ( value > 0.0 )
{
counter += 1.0;
}
else if ( value > -1.0 )
{
counter += ( 1.0 + value );
}
}
++iter;
}
MeasureType output = counter * m_Weights[0];
// std::cout << output << " ";
// std::cout << std::endl;
return output;
}
/**
*
*/
template <class TFeatureImage, class TOutputPixel>
typename ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::MeasureType
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::ComputeLogShapePriorTerm( const ParametersType & parameters ) const
{
// assume the shape parameters is from a independent gaussian distributions
MeasureType measure = 0.0;
for ( unsigned int j = 0; j < this->m_ShapeFunction->GetNumberOfShapeParameters(); j++ )
{
measure += vnl_math_sqr( ( parameters[j] - m_ShapeParameterMeans[j] ) /
m_ShapeParameterStandardDeviations[j] );
}
measure *= m_Weights[2];
// std::cout << parameters << ": ";
// std::cout << measure << " ";
return measure;
}
/**
*
*/
template <class TFeatureImage, class TOutputPixel>
typename ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::MeasureType
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::ComputeLogGradientTerm( const ParametersType & parameters ) const
{
this->m_ShapeFunction->SetParameters( parameters );
typename NodeContainerType::ConstIterator iter = this->GetActiveRegion()->Begin();
typename NodeContainerType::ConstIterator end = this->GetActiveRegion()->End();
MeasureType sum = 0.0;
// Assume that ( 1 - FeatureImage ) approximates a Gaussian (zero mean, unit variance)
// along the normal of the evolving contour.
// The GradientTerm is then given by a Laplacian of the goodness of fit of
// the Gaussian.
while( iter != end )
{
NodeType node = iter.Value();
typename ShapeFunctionType::PointType point;
this->GetFeatureImage()->TransformIndexToPhysicalPoint( node.GetIndex(), point );
sum += vnl_math_sqr( m_GaussianFunction->Evaluate( this->m_ShapeFunction->Evaluate( point ) )
-1.0 + this->GetFeatureImage()->GetPixel( node.GetIndex() ) );
++iter;
}
sum *= m_Weights[1];
// std::cout << sum << " ";
return sum;
}
/**
*
*/
template <class TFeatureImage, class TOutputPixel>
typename ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::MeasureType
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::ComputeLogPosePriorTerm( const ParametersType & itkNotUsed(parameters) ) const
{
return 0.0;
}
/**
*
*/
template <class TFeatureImage, class TOutputPixel>
void
ShapePriorMAPCostFunction<TFeatureImage,TOutputPixel>
::Initialize(void) throw ( ExceptionObject )
{
this->Superclass::Initialize();
// check if the mean and variances array are of the right size
if ( m_ShapeParameterMeans.Size() <
this->m_ShapeFunction->GetNumberOfShapeParameters() )
{
itkExceptionMacro( << "ShapeParameterMeans does not have at least "
<< this->m_ShapeFunction->GetNumberOfShapeParameters()
<< " number of elements." );
}
if ( m_ShapeParameterStandardDeviations.Size() <
this->m_ShapeFunction->GetNumberOfShapeParameters() )
{
itkExceptionMacro( << "ShapeParameterStandardDeviations does not have at least "
<< this->m_ShapeFunction->GetNumberOfShapeParameters()
<< " number of elements." );
}
}
} // end namespace itk
#endif
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