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itkMutualInformationImageToImageMetric.txx
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itkMutualInformationImageToImageMetric.txx
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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkMutualInformationImageToImageMetric.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 _itkMutualInformationImageToImageMetric_txx
#define _itkMutualInformationImageToImageMetric_txx
#include "itkMutualInformationImageToImageMetric.h"
#include "itkCovariantVector.h"
#include "itkImageRandomConstIteratorWithIndex.h"
#include "vnl/vnl_math.h"
#include "itkGaussianKernelFunction.h"
namespace itk
{
/*
* Constructor
*/
template < class TFixedImage, class TMovingImage >
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::MutualInformationImageToImageMetric()
{
m_NumberOfSpatialSamples = 0;
this->SetNumberOfSpatialSamples( 50 );
m_KernelFunction = dynamic_cast<KernelFunction*>(
GaussianKernelFunction::New().GetPointer() );
m_FixedImageStandardDeviation = 0.4;
m_MovingImageStandardDeviation = 0.4;
m_MinProbability = 0.0001;
//
// Following initialization is related to
// calculating image derivatives
this->SetComputeGradient(false); // don't use the default gradient for now
m_DerivativeCalculator = DerivativeFunctionType::New();
}
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "NumberOfSpatialSamples: ";
os << m_NumberOfSpatialSamples << std::endl;
os << indent << "FixedImageStandardDeviation: ";
os << m_FixedImageStandardDeviation << std::endl;
os << indent << "MovingImageStandardDeviation: ";
os << m_MovingImageStandardDeviation << std::endl;
os << indent << "KernelFunction: ";
os << m_KernelFunction.GetPointer() << std::endl;
}
/*
* Set the number of spatial samples
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::SetNumberOfSpatialSamples(
unsigned int num )
{
if ( num == m_NumberOfSpatialSamples ) return;
this->Modified();
// clamp to minimum of 1
m_NumberOfSpatialSamples = ((num > 1) ? num : 1 );
// resize the storage vectors
m_SampleA.resize( m_NumberOfSpatialSamples );
m_SampleB.resize( m_NumberOfSpatialSamples );
}
/*
* Uniformly sample the fixed image domain. Each sample consists of:
* - the fixed image value
* - the corresponding moving image value
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::SampleFixedImageDomain(
SpatialSampleContainer& samples ) const
{
typedef ImageRandomConstIteratorWithIndex<FixedImageType> RandomIterator;
RandomIterator randIter( this->m_FixedImage, this->GetFixedImageRegion() );
randIter.SetNumberOfSamples( m_NumberOfSpatialSamples );
randIter.GoToBegin();
typename SpatialSampleContainer::iterator iter;
typename SpatialSampleContainer::const_iterator end = samples.end();
bool allOutside = true;
this->m_NumberOfPixelsCounted = 0; // Number of pixels that map into the
// fixed and moving image mask, if specified
// and the resampled fixed grid after
// transformation.
// Number of random picks made from the portion of fixed image within the fixed mask
unsigned long numberOfFixedImagePixelsVisited = 0;
unsigned long dryRunTolerance = this->GetFixedImageRegion().GetNumberOfPixels();
for( iter=samples.begin(); iter != end; ++iter )
{
// Get sampled index
FixedImageIndexType index = randIter.GetIndex();
// Get sampled fixed image value
(*iter).FixedImageValue = randIter.Get();
// Translate index to point
this->m_FixedImage->TransformIndexToPhysicalPoint( index,
(*iter).FixedImagePointValue );
// If not inside the fixed mask, ignore the point
if( this->m_FixedImageMask &&
!this->m_FixedImageMask->IsInside( (*iter).FixedImagePointValue ) )
{
++randIter; // jump to another random position
continue;
}
if( allOutside )
{
++numberOfFixedImagePixelsVisited;
if( numberOfFixedImagePixelsVisited > dryRunTolerance )
{
// We randomly visited as many points as is the size of the fixed image
// region.. Too may samples mapped ouside.. go change your transform
itkExceptionMacro( << "Too many samples mapped outside the moving buffer" );
}
}
MovingImagePointType mappedPoint =
this->m_Transform->TransformPoint( (*iter).FixedImagePointValue );
// If the transformed point after transformation does not lie within the
// MovingImageMask, skip it.
if( this->m_MovingImageMask &&
!this->m_MovingImageMask->IsInside( mappedPoint ) )
{
++randIter;
continue;
}
// The interpolator does not need to do bounds checking if we have masks,
// since we know that the point is within the fixed and moving masks. But
// a crazy user can specify masks that are bigger than the image. Then we
// will need bounds checking.. So keep this anyway.
if( this->m_Interpolator->IsInsideBuffer( mappedPoint ) )
{
(*iter).MovingImageValue = this->m_Interpolator->Evaluate( mappedPoint );
this->m_NumberOfPixelsCounted++;
allOutside = false;
}
else
{
(*iter).MovingImageValue = 0;
}
// Jump to random position
++randIter;
}
if( allOutside )
{
// if all the samples mapped to the outside throw an exception
itkExceptionMacro(<<"All the sampled point mapped to outside of the moving image" );
}
}
/*
* Get the match Measure
*/
template < class TFixedImage, class TMovingImage >
typename MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::MeasureType
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::GetValue( const ParametersType& parameters ) const
{
// make sure the transform has the current parameters
this->m_Transform->SetParameters( parameters );
// collect sample set A
this->SampleFixedImageDomain( m_SampleA );
// collect sample set B
this->SampleFixedImageDomain( m_SampleB );
// calculate the mutual information
double dLogSumFixed = 0.0;
double dLogSumMoving = 0.0;
double dLogSumJoint = 0.0;
typename SpatialSampleContainer::const_iterator aiter;
typename SpatialSampleContainer::const_iterator aend = m_SampleA.end();
typename SpatialSampleContainer::const_iterator biter;
typename SpatialSampleContainer::const_iterator bend = m_SampleB.end();
for( biter = m_SampleB.begin() ; biter != bend; ++biter )
{
double dSumFixed = m_MinProbability;
double dSumMoving = m_MinProbability;
double dSumJoint = m_MinProbability;
for( aiter = m_SampleA.begin() ; aiter != aend; ++aiter )
{
double valueFixed;
double valueMoving;
valueFixed = ( (*biter).FixedImageValue - (*aiter).FixedImageValue ) /
m_FixedImageStandardDeviation;
valueFixed = m_KernelFunction->Evaluate( valueFixed );
valueMoving = ( (*biter).MovingImageValue - (*aiter).MovingImageValue ) /
m_MovingImageStandardDeviation;
valueMoving = m_KernelFunction->Evaluate( valueMoving );
dSumFixed += valueFixed;
dSumMoving += valueMoving;
dSumJoint += valueFixed * valueMoving;
} // end of sample A loop
dLogSumFixed -= ( dSumFixed > 0.0 ) ? log( dSumFixed ) : 0.0;
dLogSumMoving -= ( dSumMoving> 0.0 ) ? log( dSumMoving ) : 0.0;
dLogSumJoint -= ( dSumJoint > 0.0 ) ? log( dSumJoint ) : 0.0;
} // end of sample B loop
double nsamp = double( m_NumberOfSpatialSamples );
double threshold = -0.5 * nsamp * log( m_MinProbability );
if( dLogSumMoving > threshold || dLogSumFixed > threshold ||
dLogSumJoint > threshold )
{
// at least half the samples in B did not occur within
// the Parzen window width of samples in A
itkExceptionMacro(<<"Standard deviation is too small" );
}
MeasureType measure = dLogSumFixed + dLogSumMoving - dLogSumJoint;
measure /= nsamp;
measure += log( nsamp );
return measure;
}
/*
* Get the both Value and Derivative Measure
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::GetValueAndDerivative(
const ParametersType& parameters,
MeasureType& value,
DerivativeType& derivative) const
{
value = NumericTraits< MeasureType >::Zero;
unsigned int numberOfParameters = this->m_Transform->GetNumberOfParameters();
DerivativeType temp( numberOfParameters );
temp.Fill( 0 );
derivative = temp;
// make sure the transform has the current parameters
this->m_Transform->SetParameters( parameters );
// set the DerivativeCalculator
m_DerivativeCalculator->SetInputImage( this->m_MovingImage );
// collect sample set A
this->SampleFixedImageDomain( m_SampleA );
// collect sample set B
this->SampleFixedImageDomain( m_SampleB );
// calculate the mutual information
double dLogSumFixed = 0.0;
double dLogSumMoving = 0.0;
double dLogSumJoint = 0.0;
typename SpatialSampleContainer::iterator aiter;
typename SpatialSampleContainer::const_iterator aend = m_SampleA.end();
typename SpatialSampleContainer::iterator biter;
typename SpatialSampleContainer::const_iterator bend = m_SampleB.end();
// precalculate all the image derivatives for sample A
typedef std::vector<DerivativeType> DerivativeContainer;
DerivativeContainer sampleADerivatives;
sampleADerivatives.resize( m_NumberOfSpatialSamples );
typename DerivativeContainer::iterator aditer;
DerivativeType tempDeriv( numberOfParameters );
for( aiter = m_SampleA.begin(), aditer = sampleADerivatives.begin();
aiter != aend; ++aiter, ++aditer )
{
/*** FIXME: is there a way to avoid the extra copying step? *****/
this->CalculateDerivatives( (*aiter).FixedImagePointValue, tempDeriv );
(*aditer) = tempDeriv;
}
DerivativeType derivB(numberOfParameters);
for( biter = m_SampleB.begin(); biter != bend; ++biter )
{
double dDenominatorMoving = m_MinProbability;
double dDenominatorJoint = m_MinProbability;
double dSumFixed = m_MinProbability;
for( aiter = m_SampleA.begin(); aiter != aend; ++aiter )
{
double valueFixed;
double valueMoving;
valueFixed = ( (*biter).FixedImageValue - (*aiter).FixedImageValue )
/ m_FixedImageStandardDeviation;
valueFixed = m_KernelFunction->Evaluate( valueFixed );
valueMoving = ( (*biter).MovingImageValue - (*aiter).MovingImageValue )
/ m_MovingImageStandardDeviation;
valueMoving = m_KernelFunction->Evaluate( valueMoving );
dDenominatorMoving += valueMoving;
dDenominatorJoint += valueMoving * valueFixed;
dSumFixed += valueFixed;
} // end of sample A loop
if( dSumFixed > 0.0 )
{
dLogSumFixed -= log( dSumFixed );
}
if( dDenominatorMoving > 0.0 )
{
dLogSumMoving -= log( dDenominatorMoving );
}
if( dDenominatorJoint > 0.0 )
{
dLogSumJoint -= log( dDenominatorJoint );
}
// get the image derivative for this B sample
this->CalculateDerivatives( (*biter).FixedImagePointValue, derivB );
double totalWeight = 0.0;
for( aiter = m_SampleA.begin(), aditer = sampleADerivatives.begin();
aiter != aend; ++aiter, ++aditer )
{
double valueFixed;
double valueMoving;
double weightMoving;
double weightJoint;
double weight;
valueFixed = ( (*biter).FixedImageValue - (*aiter).FixedImageValue ) /
m_FixedImageStandardDeviation;
valueFixed = m_KernelFunction->Evaluate( valueFixed );
valueMoving = ( (*biter).MovingImageValue - (*aiter).MovingImageValue ) /
m_MovingImageStandardDeviation;
valueMoving = m_KernelFunction->Evaluate( valueMoving );
weightMoving = valueMoving / dDenominatorMoving;
weightJoint = valueMoving * valueFixed / dDenominatorJoint;
weight = ( weightMoving - weightJoint );
weight *= (*biter).MovingImageValue - (*aiter).MovingImageValue;
totalWeight += weight;
derivative -= (*aditer) * weight;
} // end of sample A loop
derivative += derivB * totalWeight;
} // end of sample B loop
double nsamp = double( m_NumberOfSpatialSamples );
double threshold = -0.5 * nsamp * log( m_MinProbability );
if( dLogSumMoving > threshold || dLogSumFixed > threshold ||
dLogSumJoint > threshold )
{
// at least half the samples in B did not occur within
// the Parzen window width of samples in A
itkExceptionMacro(<<"Standard deviation is too small" );
}
value = dLogSumFixed + dLogSumMoving - dLogSumJoint;
value /= nsamp;
value += log( nsamp );
derivative /= nsamp;
derivative /= vnl_math_sqr( m_MovingImageStandardDeviation );
}
/*
* Get the match measure derivative
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::GetDerivative( const ParametersType& parameters, DerivativeType & derivative ) const
{
MeasureType value;
// call the combined version
this->GetValueAndDerivative( parameters, value, derivative );
}
/*
* Calculate derivatives of the image intensity with respect
* to the transform parmeters.
*
* This should really be done by the mapper.
*
* This is a temporary solution until this feature is implemented
* in the mapper. This solution only works for any transform
* that support GetJacobian()
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::CalculateDerivatives(
const FixedImagePointType& point,
DerivativeType& derivatives ) const
{
MovingImagePointType mappedPoint = this->m_Transform->TransformPoint( point );
CovariantVector<double,MovingImageDimension> imageDerivatives;
if ( m_DerivativeCalculator->IsInsideBuffer( mappedPoint ) )
{
imageDerivatives = m_DerivativeCalculator->Evaluate( mappedPoint );
}
else
{
derivatives.Fill( 0.0 );
return;
}
typedef typename TransformType::JacobianType JacobianType;
const JacobianType& jacobian = this->m_Transform->GetJacobian( point );
unsigned int numberOfParameters = this->m_Transform->GetNumberOfParameters();
for ( unsigned int k = 0; k < numberOfParameters; k++ )
{
derivatives[k] = 0.0;
for ( unsigned int j = 0; j < MovingImageDimension; j++ )
{
derivatives[k] += jacobian[j][k] * imageDerivatives[j];
}
}
}
/*
* Reinitialize the seed of the random number generator
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::ReinitializeSeed()
{
Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()->SetSeed();
}
/*
* Reinitialize the seed of the random number generator
*/
template < class TFixedImage, class TMovingImage >
void
MutualInformationImageToImageMetric<TFixedImage,TMovingImage>
::ReinitializeSeed(int seed)
{
Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()->SetSeed(seed);
}
} // end namespace itk
#endif