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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkKullbackLeiblerCompareHistogramImageToImageMetric_hxx
#define itkKullbackLeiblerCompareHistogramImageToImageMetric_hxx
#include "itkKullbackLeiblerCompareHistogramImageToImageMetric.h"
#include "itkHistogram.h"
// Todo: need to access Use_Padding in parent. Make in protected
// need to figure out what to do when "stuff" is not in histogram
// kernel function?
namespace itk
{
template< typename TFixedImage, typename TMovingImage >
KullbackLeiblerCompareHistogramImageToImageMetric< TFixedImage,
TMovingImage >::KullbackLeiblerCompareHistogramImageToImageMetric()
{
m_Epsilon = 1e-12; // should be smaller than 1/numBins^2
}
template< typename TFixedImage, typename TMovingImage >
void
KullbackLeiblerCompareHistogramImageToImageMetric< TFixedImage, TMovingImage >
::Initialize()
{
Superclass::Initialize();
}
template< typename TFixedImage, typename TMovingImage >
typename KullbackLeiblerCompareHistogramImageToImageMetric< TFixedImage, \
TMovingImage >::MeasureType
KullbackLeiblerCompareHistogramImageToImageMetric< TFixedImage, \
TMovingImage >
::EvaluateMeasure(HistogramType & histogram) const
{
// Two terms.
// First the term that measures the entropy of the term
// p(x,y) log p(x,y) - p(x,y) log q(x,y)
MeasureType KullbackLeibler = NumericTraits< MeasureType >::ZeroValue();
HistogramIteratorType measured_it = histogram.Begin();
HistogramIteratorType measured_end = histogram.End();
HistogramIteratorType training_it = this->GetTrainingHistogram()->Begin();
HistogramIteratorType training_end = this->GetTrainingHistogram()->End();
while ( measured_it != measured_end )
{
// Every bin gets epsilon added to it
double TrainingFreq = training_it.GetFrequency() + m_Epsilon;
double MeasuredFreq = measured_it.GetFrequency() + m_Epsilon;
KullbackLeibler += MeasuredFreq * std::log(MeasuredFreq / TrainingFreq);
++measured_it;
++training_it;
}
if ( training_it != training_end )
{
itkWarningMacro("The Measured and Training Histograms have different number of bins.");
}
// Get the total frequency for each histogram.
HistogramFrequencyType totalTrainingFreq = this->GetTrainingHistogram()->GetTotalFrequency();
HistogramFrequencyType totalMeasuredFreq = histogram.GetTotalFrequency();
// The actual number of total frequency is a bit larger
// than the number of counts because we add m_Epsilon to every bin
double AdjustedTotalTrainingFreq = totalTrainingFreq
+ this->GetHistogramSize()[0] * this->GetHistogramSize()[1] * m_Epsilon;
double AdjustedTotalMeasuredFreq = totalMeasuredFreq
+ this->GetHistogramSize()[0] * this->GetHistogramSize()[1] * m_Epsilon;
KullbackLeibler = KullbackLeibler / static_cast< MeasureType >( AdjustedTotalMeasuredFreq )
- std::log(AdjustedTotalMeasuredFreq / AdjustedTotalTrainingFreq);
return KullbackLeibler;
}
template< typename TFixedImage, typename TMovingImage >
void KullbackLeiblerCompareHistogramImageToImageMetric< TFixedImage, TMovingImage >::PrintSelf(std::ostream & os,
Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Epsilon: " << m_Epsilon << std::endl;
}
} // End namespace itk
#endif // itkKullbackLeiblerCompareHistogramImageToImageMetric_hxx
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