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itkBayesianClassifierImageFilter.h
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itkBayesianClassifierImageFilter.h
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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkBayesianClassifierImageFilter.h
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 __itkBayesianClassifierImageFilter_h
#define __itkBayesianClassifierImageFilter_h
#include "itkVectorImage.h"
#include "itkImageToImageFilter.h"
#include "itkMaximumDecisionRule.h"
#include "itkImageRegionIterator.h"
#include "itkImageRegionConstIterator.h"
namespace itk
{
/** \class BayesianClassifierImageFilter
*
* \brief Performs Bayesian Classification on an image.
*
* \par Inputs and Outputs
* The input to this filter is an itk::VectorImage that represents pixel
* memberships to 'n' classes. This image is conveniently generated by the
* BayesianClassifierInitializationImageFilter. You may use that filter to
* generate the membership images or specify your own.
*
* \par
* The output of the filter is a label map (an image of unsigned char's is the
* default.) with pixel values indicating the classes they correspond to. Pixels
* with intensity 0 belong to the 0th class, 1 belong to the 1st class etc....
* The classification is done by applying a Maximum decision rule to the posterior
* image.
*
* \par Parameters
* The filter optionally allows you to specify a prior image as well. The prior
* image, if specified must be a VectorImage with as many components as the
* number of classes. The posterior image is then generated by multiplying the
* prior image with the membership image. If the prior image is not specified,
* the posterior image is the same as the membership image. Another way to
* look at it is that the priors default to having a uniform distribution over
* the number of classes.
* Posterior membership of a pixel = Prior * Membership
*
* \par
* The filter optionally accepts a smoothing filter and number of iterations
* associated with the smoothing filter.
* The philosophy is that the filter allows you to iteratively
* smooth the posteriors prior to applying the decision rule. It is hoped
* that this would yield a better classification. The user will need to plug
* in his own smoothing filter with all the parameters set.
*
* \par Template parameters
* InputVectorImage, datatype of the output labelmap, precision of the posterior
* image, precision of the prior image.
*
* \author John Melonakos, Georgia Tech
*
* \note
* This work is part of the National Alliance for Medical Image Computing
* (NAMIC), funded by the National Institutes of Health through the NIH Roadmap
* for Medical Research, Grant U54 EB005149.
*
* \sa VectorImage
* \sa BayesianClassifierInitializationImageFilter
* \ingroup ClassificationFilters
*/
template < class TInputVectorImage, class TLabelsType=unsigned char,
class TPosteriorsPrecisionType=double, class TPriorsPrecisionType=double >
class ITK_EXPORT BayesianClassifierImageFilter :
public ImageToImageFilter<
TInputVectorImage, Image< TLabelsType,
::itk::GetImageDimension< TInputVectorImage >::ImageDimension > >
{
public:
/** Standard class typedefs. */
typedef BayesianClassifierImageFilter Self;
typedef ImageToImageFilter<
TInputVectorImage,
Image< TLabelsType,
::itk::GetImageDimension<
TInputVectorImage >::ImageDimension > > 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( BayesianClassifierImageFilter, ImageToImageFilter );
/** Input and Output image types */
typedef typename Superclass::InputImageType InputImageType;
/** Dimension of the input image */
itkStaticConstMacro( Dimension, unsigned int,
::itk::GetImageDimension< InputImageType >::ImageDimension );
typedef Image< TLabelsType,
itkGetStaticConstMacro(Dimension) > OutputImageType;
typedef typename InputImageType::ConstPointer InputImagePointer;
typedef typename OutputImageType::Pointer OutputImagePointer;
typedef typename InputImageType::RegionType ImageRegionType;
/** Input and Output image iterators */
typedef ImageRegionConstIterator< InputImageType > InputImageIteratorType;
typedef ImageRegionIterator< OutputImageType > OutputImageIteratorType;
/** Pixel types. */
typedef typename InputImageType::PixelType InputPixelType;
typedef typename OutputImageType::PixelType OutputPixelType;
/** Image Type and Pixel type for the images representing the Prior
* probability of a pixel belonging to a particular class. This image has
* arrays as pixels, the number of elements in the array is the same as the
* number of classes to be used. */
typedef VectorImage< TPriorsPrecisionType,
itkGetStaticConstMacro(Dimension) > PriorsImageType;
typedef typename PriorsImageType::PixelType PriorsPixelType;
typedef typename PriorsImageType::Pointer PriorsImagePointer;
typedef ImageRegionConstIterator< PriorsImageType > PriorsImageIteratorType;
/** Image Type and Pixel type for the images representing the membership of a
* pixel to a particular class. This image has arrays as pixels, the number of
* elements in the array is the same as the number of classes to be used. */
typedef TInputVectorImage MembershipImageType;
typedef typename MembershipImageType::PixelType MembershipPixelType;
typedef typename MembershipImageType::Pointer MembershipImagePointer;
typedef ImageRegionConstIterator< MembershipImageType > MembershipImageIteratorType;
/** Image Type and Pixel type for the images representing the Posterior
* probability of a pixel belonging to a particular class. This image has
* arrays as pixels, the number of elements in the array is the same as the
* number of classes to be used. */
typedef VectorImage< TPosteriorsPrecisionType,
itkGetStaticConstMacro(Dimension) > PosteriorsImageType;
typedef typename PosteriorsImageType::PixelType PosteriorsPixelType;
typedef typename PosteriorsImageType::Pointer PosteriorsImagePointer;
typedef ImageRegionIterator< PosteriorsImageType > PosteriorsImageIteratorType;
/** Decision rule to use for defining the label */
typedef MaximumDecisionRule DecisionRuleType;
typedef DecisionRuleType::Pointer DecisionRulePointer;
/** An image from a single component of the Posterior */
typedef itk::Image< TPosteriorsPrecisionType,
itkGetStaticConstMacro(Dimension) > ExtractedComponentImageType;
/** Optional Smoothing filter that will be applied to the Posteriors */
typedef ImageToImageFilter<
ExtractedComponentImageType,
ExtractedComponentImageType > SmoothingFilterType;
typedef typename SmoothingFilterType::Pointer SmoothingFilterPointer;
/** Set/ Get macros for the smoothing filter that may optionally be applied
* to the posterior image */
void SetSmoothingFilter( SmoothingFilterType * );
itkGetMacro( SmoothingFilter, SmoothingFilterPointer );
/** Number of iterations to apply the smoothing filter */
itkSetMacro( NumberOfSmoothingIterations, unsigned int );
itkGetMacro( NumberOfSmoothingIterations, unsigned int );
#ifdef ITK_USE_CONCEPT_CHECKING
/** Begin concept checking */
itkConceptMacro(UnsignedIntConvertibleToLabelsCheck,
(Concept::Convertible<unsigned int, TLabelsType>));
itkConceptMacro(PosteriorsAdditiveOperatorsCheck,
(Concept::AdditiveOperators<TPosteriorsPrecisionType>));
itkConceptMacro(IntConvertibleToPosteriorsCheck,
(Concept::Convertible<int, TPosteriorsPrecisionType>));
itkConceptMacro(InputHasNumericTraitsCheck,
(Concept::HasNumericTraits<typename InputPixelType::ValueType>));
itkConceptMacro(PosteriorsHasNumericTraitsCheck,
(Concept::HasNumericTraits<TPosteriorsPrecisionType>));
itkConceptMacro(PriorsHasNumericTraitsCheck,
(Concept::HasNumericTraits<TPriorsPrecisionType>));
itkConceptMacro(InputPriorsPosteriorsMultiplyOperatorCheck,
(Concept::MultiplyOperator<typename InputPixelType::ValueType,
PriorsPixelType, PosteriorsPixelType>));
/** End concept checking */
#endif
protected:
BayesianClassifierImageFilter();
virtual ~BayesianClassifierImageFilter() {}
void PrintSelf(std::ostream& os, Indent indent) const;
/** Here is where the classification is computed. */
virtual void GenerateData();
/** Allocate Memory for the Output. */
virtual void AllocateOutputs();
/** Methods for computing the labeled map for all combinations of conditions */
virtual void ComputeBayesRule();
virtual void NormalizeAndSmoothPosteriors();
virtual void ClassifyBasedOnPosteriors();
PosteriorsImageType *GetPosteriorImage();
private:
BayesianClassifierImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
/** Boolean flag indicating that the user defined the Priors optional input */
bool m_UserProvidedPriors;
/** Boolean flag indicating that the user provided a Smoothing filter */
bool m_UserProvidedSmoothingFilter;
/** Pointer to optional Smoothing filter */
SmoothingFilterPointer m_SmoothingFilter;
/** Number of iterations to apply the smoothing filter */
unsigned int m_NumberOfSmoothingIterations;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkBayesianClassifierImageFilter.txx"
#endif
#endif