itkgreylevelcooccurrencematrixtexturecoefficientscalculator.h

DTMK软件开发包,此为开源软件,是一款很好的医学图像开发资源.

/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h,v $
  Language:  C++
  Date:      $Date: 2005-04-20 20:31:22 $
  Version:   $Revision: 1.4 $

  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:
    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