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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h,v $
Language: C++
Date: $Date: 2009-05-20 16:21:47 $
Version: $Revision: 1.6 $
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_h
#define __itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator_h
#include "itkHistogram.h"
#include "itkMacro.h"
namespace itk {
namespace Statistics {
/** \class GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator
* \brief This class computes texture feature coefficients from a grey level
* co-occurrence matrix.
*
* This class computes features that summarize image texture, given a grey level
* co-occurrence matrix (generated by a ScalarImageToGreyLevelCooccurrenceMatrixGenerator
* or related class).
*
* The features calculated are as follows (where \f$ g(i, j) \f$ is the element in
* cell i, j of a a normalized GLCM):
*
* "Energy" \f$ = f_1 = \sum_{i,j}g(i, j)^2 \f$
*
* "Entropy" \f$ = f_2 = -\sum_{i,j}g(i, j) \log_2 g(i, j)\f$, or 0 if \f$g(i, j) = 0\f$
*
* "Correlation" \f$ = f_3 = \sum_{i,j}\frac{(i - \mu)(j - \mu)g(i, j)}{\sigma^2} \f$
*
* "Difference Moment" \f$= f_4 = \sum_{i,j}\frac{1}{1 + (i - j)^2}g(i, j) \f$
*
* "Inertia" \f$ = f_5 = \sum_{i,j}(i - j)^2g(i, j) \f$ (sometimes called "contrast.")
*
* "Cluster Shade" \f$ = f_6 = \sum_{i,j}((i - \mu) + (j - \mu))^3 g(i, j) \f$
*
* "Cluster Prominence" \f$ = f_7 = \sum_{i,j}((i - \mu) + (j - \mu))^4 g(i, j) \f$
*
* "Haralick's Correlation" \f$ = f_8 = \frac{\sum_{i,j}(i, j) g(i, j) -\mu_t^2}{\sigma_t^2} \f$
* where \f$\mu_t\f$ and \f$\sigma_t\f$ are the mean and standard deviation of the row
* (or column, due to symmetry) sums.
*
* Above, \f$ \mu = \f$ (weighted pixel average) \f$ = \sum_{i,j}i \cdot g(i, j) =
* \sum_{i,j}j \cdot g(i, j) \f$ (due to matrix summetry), and
*
* \f$ \sigma = \f$ (weighted pixel variance) \f$ = \sum_{i,j}(i - \mu)^2 \cdot g(i, j) =
* \sum_{i,j}(j - \mu)^2 \cdot g(i, j) \f$ (due to matrix summetry)
*
* A good texture feature set to use is the Conners, Trivedi and Harlow set:
* features 1, 2, 4, 5, 6, and 7. There is some correlation between the various
* features, so using all of them at the same time is not necessarialy a good idea.
*
* NOTA BENE: The input histogram will be forcably normalized!
* This algorithm takes three passes through the input
* histogram if the histogram was already normalized, and four if not.
*
* Web references:
*
* http://www.cssip.uq.edu.au/meastex/www/algs/algs/algs.html
* http://www.ucalgary.ca/~mhallbey/texture/texture_tutorial.html
*
* Print references:
*
* Haralick, R.M., K. Shanmugam and I. Dinstein. 1973. Textural Features for
* Image Classification. IEEE Transactions on Systems, Man and Cybernetics.
* SMC-3(6):610-620.
*
* Haralick, R.M. 1979. Statistical and Structural Approaches to Texture.
* Proceedings of the IEEE, 67:786-804.
*
* R.W. Conners and C.A. Harlow. A Theoretical Comaprison of Texture Algorithms.
* IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:204-222, 1980.
*
* R.W. Conners, M.M. Trivedi, and C.A. Harlow. Segmentation of a High-Resolution
* Urban Scene using Texture Operators. Computer Vision, Graphics and Image
* Processing, 25:273-310, 1984.
*
* \sa ScalarImageToGreyLevelCooccurrenceMatrixGenerator
* \sa MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator
* \sa ScalarImageTextureCalculator
*
* Author: Zachary Pincus
*/
/** Texture feature types */
enum TextureFeatureName { Energy, Entropy, Correlation,
InverseDifferenceMoment, Inertia, ClusterShade, ClusterProminence,
HaralickCorrelation };
template< class THistogram >
class GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator : public Object
{
public:
/** Standard typedefs */
typedef GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator Self;
typedef Object Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Run-time type information (and related methods). */
itkTypeMacro(GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator, Object);
/** standard New() method support */
itkNewMacro(Self);
typedef THistogram HistogramType;
typedef typename HistogramType::Pointer HistogramPointer;
typedef typename HistogramType::ConstPointer HistogramConstPointer;
typedef typename HistogramType::MeasurementType MeasurementType;
typedef typename HistogramType::MeasurementVectorType MeasurementVectorType;
typedef typename HistogramType::IndexType IndexType;
typedef typename HistogramType::FrequencyType FrequencyType;
/** Triggers the Computation of the histogram */
void Compute( void );
/** Connects the GLCM histogram over which the features are going to be computed */
itkSetObjectMacro( Histogram, HistogramType );
itkGetObjectMacro( Histogram, HistogramType );
/** Methods to return the feature values.
\warning These outputs are only valid after the Compute() method has been invoked
\sa Compute */
double GetFeature(TextureFeatureName feature);
itkGetMacro(Energy, double);
itkGetMacro(Entropy, double);
itkGetMacro(Correlation, double);
itkGetMacro(InverseDifferenceMoment, double);
itkGetMacro(Inertia, double);
itkGetMacro(ClusterShade, double);
itkGetMacro(ClusterProminence, double);
itkGetMacro(HaralickCorrelation, double);
protected:
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator() {};
virtual ~GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator() {};
void PrintSelf(std::ostream& os, Indent indent) const;
private:
GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
HistogramPointer m_Histogram;
double m_Energy, m_Entropy, m_Correlation, m_InverseDifferenceMoment,
m_Inertia, m_ClusterShade, m_ClusterProminence, m_HaralickCorrelation;
void NormalizeHistogram(void);
void ComputeMeansAndVariances( double &pixelMean, double &marginalMean,
double &marginalDevSquared, double &pixelVariance );
};
} // end of namespace Statistics
} // end of namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.txx"
#endif
#endif
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