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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_h
#define itkKullbackLeiblerCompareHistogramImageToImageMetric_h

#include "itkCompareHistogramImageToImageMetric.h"

namespace itk
{
/** \class KullbackLeiblerCompareHistogramImageToImageMetric
 *  \brief Computes the Kubler Lieblach(KL) metric between the histogram
 *  of the two images to be registered and a training histogram.
 *
 *  This class is templated over the type of the fixed and moving
 *  images to be compared.
 *
 *  This class computers the KL-metric by comparing the histograms
 *  of the testing histogram formed by the overlap of intensities in
 *  the images, to a training histogram. It is based on the
 *  following paper:
 *
 *  Albert C.S. Chung, William M. Wells III, Alexander Norbash, and
 *  W. Eric L.  Grimson, Multi-modal Image Registration by
 *  Minimising Kullback-Leibler Distance, In Medical Image Computing
 *  and Computer-Assisted Intervention - MICCAI 2002, LNCS 2489,
 *  pp. 525 - 532.
 *
 *  The metric is given by KL(P_test||P_train)
 *  = Sum_{i1,i2} P_test(i1,i2) std::log(P_test(i1,i2)/P_train(i1,i2))
 *  where P_test and P_train are probabilities given my normalized
 *  histograms, and i1 and i2 are the intensity bins in the histogram.
 *
 *  \par PARAMETERS
 *  Epsilon is added to every bin in both histograms. This prevents
 *  division by zero problems. Epsilon should generally be set to a
 *  number smaller than one divided by the total number bins in
 *  the histogram. So, for a 256 by 256 histogram, Epsilon should be
 *  much less than 1e-5. Tests have shown that choices of epsilon are
 *  not very important as long as it is small enough. The default is 1e-12.
 *  I doubt you will need to change it.
 *
 *  \author Samson Timoner
 *
 *  \par SUPPORT
 *  This work was supported by the Functional Imaging Research in
 *  Schizophrenia Testbed (FIRST) Biomedical Informatics Research
 *  Network (BIRN, http://www.birncommunity.org/), which is funded by the
 *  National Center for Research Resources at the National
 *  Institutes of Health (NIH).  This work is also funded by the
 *  Neuroimage Analysis Center (P41 RR13218).
 *
 *  \ingroup RegistrationMetrics
 * \ingroup ITKRegistrationCommon
 */

template< typename TFixedImage, typename TMovingImage >
class KullbackLeiblerCompareHistogramImageToImageMetric:
  public CompareHistogramImageToImageMetric< TFixedImage, TMovingImage >
{
public:
  /** Standard class typedefs. */
  typedef KullbackLeiblerCompareHistogramImageToImageMetric Self;
  typedef CompareHistogramImageToImageMetric< TFixedImage, TMovingImage >
  Superclass;
  typedef SmartPointer< Self >       Pointer;
  typedef SmartPointer< const Self > ConstPointer;

  /** Method for creation through the object factory. */
  itkNewMacro(Self);

  /** Run-time type information (and related methods). */
  itkTypeMacro(KullbackLeiblerCompareHistogramImageToImageMetric,
               HistogramImageToImageMetric);

  /** Types transferred from the base class */
  typedef typename Superclass::RealType                RealType;
  typedef typename Superclass::TransformType           TransformType;
  typedef typename Superclass::TransformPointer        TransformPointer;
  typedef typename Superclass::ConstPointer            TransformConstPointer;
  typedef typename Superclass::TransformParametersType TransformParametersType;
  typedef typename Superclass::TransformJacobianType   TransformJacobianType;
  typedef typename Superclass::GradientPixelType       GradientPixelType;

  typedef typename Superclass::MeasureType            MeasureType;
  typedef typename Superclass::DerivativeType         DerivativeType;
  typedef typename Superclass::FixedImageType         FixedImageType;
  typedef typename Superclass::MovingImageType        MovingImageType;
  typedef typename Superclass::FixedImageConstPointer FixedImageConstPointer;
  typedef typename Superclass::MovingImageConstPointer
  MovingImageConstPointer;

  typedef typename Superclass::HistogramType     HistogramType;
  typedef typename Superclass::HistogramSizeType HistogramSizeType;
  typedef typename Superclass::MeasurementVectorType
  HistogramMeasurementVectorType;

  typedef typename Superclass::HistogramFrequencyType HistogramFrequencyType;
  typedef typename Superclass::HistogramIteratorType  HistogramIteratorType;
  typedef typename Superclass::HistogramPointerType   HistogramPointerType;
  typedef typename Superclass::InterpolatorType       InterpolatorType;
  typedef typename Superclass::InterpolatorPointer    InterpolatorPointer;

  /** Set epsilon, which is added to each bin in both Histogram */
  itkSetMacro(Epsilon, double);

  /** Get epsilon, the histogram frequency to use if the frequency is 0 */
  itkGetConstReferenceMacro(Epsilon, double);

  /** Return the number of parameters required by the Transform */
  unsigned int GetNumberOfParameters(void) const ITK_OVERRIDE
  { return this->GetTransform()->GetNumberOfParameters(); }

  /** Forms the histogram of the training images to prepare to evaluate the */
  /** metric. Must set all parameters first */
  void Initialize() throw ( ExceptionObject ) ITK_OVERRIDE;

protected:
  /** Constructor is protected to ensure that \c New() function is used to
      create instances. */
  KullbackLeiblerCompareHistogramImageToImageMetric();
  virtual ~KullbackLeiblerCompareHistogramImageToImageMetric(){}
  void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;

  /** Form the Histogram for the Training data */
  void FormTrainingHistogram()
  throw ( ExceptionObject );

  /** Evaluates the mutual information from the histogram. */
  virtual MeasureType EvaluateMeasure(HistogramType & histogram) const ITK_OVERRIDE;

  double m_Epsilon;

private:
  // Purposely not implemented.
  KullbackLeiblerCompareHistogramImageToImageMetric(Self const &);
  void operator=(Self const &); // Purposely not implemented.
};
} // End namespace itk.

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkKullbackLeiblerCompareHistogramImageToImageMetric.hxx"
#endif

#endif // itkKullbackLeiblerCompareHistogramImageToImageMetric_h