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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.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 __itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator_txx
#define __itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator_txx
#include "itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h"
#include "itkNumericTraits.h"
#include "vnl/vnl_math.h"
namespace itk {
namespace Statistics {
template< class THistogram >
void
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator< THistogram >::
Compute( void )
{
typedef typename HistogramType::Iterator HistogramIterator;
// First, normalize the histogram if it doesn't look normalized.
// This is one pass through the histogram.
FrequencyType totalFrequency = m_Histogram->GetTotalFrequency();
if ( (totalFrequency - NumericTraits<MeasurementType>::One) > 0.0001 )
{
// Doesn't look normalized:
this->NormalizeHistogram();
}
// Now get the various means and variances. This is takes two passes
// through the histogram.
double pixelMean, marginalMean, marginalDevSquared, pixelVariance;
this->ComputeMeansAndVariances(pixelMean, marginalMean, marginalDevSquared,
pixelVariance);
// Finally compute the texture features. Another one pass.
m_Energy = m_Entropy = m_Correlation = m_InverseDifferenceMoment =
m_Inertia = m_ClusterShade = m_ClusterProminence = m_HaralickCorrelation = 0;
double pixelVarianceSquared = pixelVariance * pixelVariance;
double log2 = vcl_log(2.);
for (HistogramIterator hit = m_Histogram->Begin();
hit != m_Histogram->End(); ++hit)
{
MeasurementType frequency = hit.GetFrequency();
if (frequency == 0)
{
continue; // no use doing these calculations if we're just multiplying by zero.
}
IndexType index = m_Histogram->GetIndex(hit.GetInstanceIdentifier());
m_Energy += frequency * frequency;
m_Entropy -= (frequency > 0.0001) ? frequency * vcl_log(frequency) / log2 : 0;
m_Correlation += ( (index[0] - pixelMean) * (index[1] - pixelMean) * frequency)
/ pixelVarianceSquared;
m_InverseDifferenceMoment += frequency /
(1.0 + (index[0] - index[1]) * (index[0] - index[1]) );
m_Inertia += (index[0] - index[1]) * (index[0] - index[1]) * frequency;
m_ClusterShade += vcl_pow((index[0] - pixelMean) + (index[1] - pixelMean), 3) *
frequency;
m_ClusterProminence += vcl_pow((index[0] - pixelMean) + (index[1] - pixelMean), 4) *
frequency;
m_HaralickCorrelation += index[0] * index[1] * frequency;
}
m_HaralickCorrelation = (m_HaralickCorrelation - marginalMean * marginalMean) /
marginalDevSquared;
}
template< class THistogram >
void
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator< THistogram >::
NormalizeHistogram( void )
{
typename HistogramType::Iterator hit;
typename HistogramType::FrequencyType totalFrequency =
m_Histogram->GetTotalFrequency();
for (hit = m_Histogram->Begin(); hit != m_Histogram->End(); ++hit)
{
hit.SetFrequency(hit.GetFrequency() / totalFrequency);
}
}
template< class THistogram >
void
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator< THistogram >::
ComputeMeansAndVariances( double &pixelMean, double &marginalMean,
double &marginalDevSquared, double &pixelVariance )
{
// This function takes two passes through the histogram and two passes through
// an array of the same length as a histogram axis. This could probably be
// cleverly compressed to one pass, but it's not clear that that's necessary.
typedef typename HistogramType::Iterator HistogramIterator;
// Initialize everything
typename HistogramType::SizeValueType binsPerAxis = m_Histogram->GetSize(0);
double *marginalSums = new double[binsPerAxis];
for (double *ms_It = marginalSums;
ms_It < marginalSums + binsPerAxis; ms_It++)
{
*ms_It = 0;
}
pixelMean = 0;
// Ok, now do the first pass through the histogram to get the marginal sums
// and compute the pixel mean
HistogramIterator hit;
for (hit = m_Histogram->Begin(); hit != m_Histogram->End(); ++hit)
{
MeasurementType frequency = hit.GetFrequency();
IndexType index = m_Histogram->GetIndex(hit.GetInstanceIdentifier());
pixelMean += index[0] * frequency;
marginalSums[index[0]] += frequency;
}
/* Now get the mean and deviaton of the marginal sums.
Compute incremental mean and SD, a la Knuth, "The Art of Computer
Programming, Volume 2: Seminumerical Algorithms", section 4.2.2.
Compute mean and standard deviation using the recurrence relation:
M(1) = x(1), M(k) = M(k-1) + (x(k) - M(k-1) ) / k
S(1) = 0, S(k) = S(k-1) + (x(k) - M(k-1)) * (x(k) - M(k))
for 2 <= k <= n, then
sigma = vcl_sqrt(S(n) / n) (or divide by n-1 for sample SD instead of
population SD).
*/
marginalMean = marginalSums[0];
marginalDevSquared = 0;
for (unsigned int arrayIndex = 1; arrayIndex < binsPerAxis; arrayIndex++)
{
int k = arrayIndex + 1;
double M_k_minus_1 = marginalMean;
double S_k_minus_1 = marginalDevSquared;
double x_k = marginalSums[arrayIndex];
double M_k = M_k_minus_1 + (x_k - M_k_minus_1) / k;
double S_k = S_k_minus_1 + (x_k - M_k_minus_1) * (x_k - M_k);
marginalMean = M_k;
marginalDevSquared = S_k;
}
marginalDevSquared = marginalDevSquared / binsPerAxis;
// OK, now compute the pixel variances.
pixelVariance = 0;
for (hit = m_Histogram->Begin(); hit != m_Histogram->End(); ++hit)
{
MeasurementType frequency = hit.GetFrequency();
IndexType index = m_Histogram->GetIndex(hit.GetInstanceIdentifier());
pixelVariance += (index[0] - pixelMean) * (index[0] - pixelMean) * frequency;
}
delete [] marginalSums;
}
template< class THistogram >
double
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator< THistogram >::
GetFeature(TextureFeatureName feature)
{
switch(feature)
{
case Energy:
return this->GetEnergy();
case Entropy:
return this->GetEntropy();
case Correlation:
return this->GetCorrelation();
case InverseDifferenceMoment:
return this->GetInverseDifferenceMoment();
case Inertia:
return this->GetInertia();
case ClusterShade:
return this->GetClusterShade();
case ClusterProminence:
return this->GetClusterProminence();
case HaralickCorrelation:
return this->GetHaralickCorrelation();
default:
return 0;
}
}
template< class THistogram >
void
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator< THistogram >::
PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os,indent);
}
} // end of namespace Statistics
} // end of namespace itk
#endif
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