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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkBayesianClassifierImageFilter_h
#define itkBayesianClassifierImageFilter_h
#include "itkVectorImage.h"
#include "itkImageToImageFilter.h"
#include "itkMaximumDecisionRule.h"
#include "itkImageRegionIterator.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
* \ingroup ITKClassifiers
*/
template <typename TInputVectorImage,
typename TLabelsType = unsigned char,
typename TPosteriorsPrecisionType = double,
typename TPriorsPrecisionType = double>
class ITK_TEMPLATE_EXPORT BayesianClassifierImageFilter
: public ImageToImageFilter<TInputVectorImage, Image<TLabelsType, TInputVectorImage::ImageDimension>>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(BayesianClassifierImageFilter);
/** Standard class type aliases. */
using Self = BayesianClassifierImageFilter;
using Superclass = ImageToImageFilter<TInputVectorImage, Image<TLabelsType, TInputVectorImage::ImageDimension>>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(BayesianClassifierImageFilter);
/** Input and Output image types. */
using typename Superclass::InputImageType;
/** Dimension of the input image. */
static constexpr unsigned int Dimension = InputImageType::ImageDimension;
using OutputImageType = Image<TLabelsType, Self::Dimension>;
using InputImagePointer = typename InputImageType::ConstPointer;
using OutputImagePointer = typename OutputImageType::Pointer;
using ImageRegionType = typename InputImageType::RegionType;
/** Input and Output image iterators. */
using InputImageIteratorType = ImageRegionConstIterator<InputImageType>;
using OutputImageIteratorType = ImageRegionIterator<OutputImageType>;
/** Pixel types. */
using InputPixelType = typename InputImageType::PixelType;
using OutputPixelType = typename OutputImageType::PixelType;
/** 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. */
using PriorsImageType = VectorImage<TPriorsPrecisionType, Self::Dimension>;
using PriorsPixelType = typename PriorsImageType::PixelType;
using PriorsImagePointer = typename PriorsImageType::Pointer;
using PriorsImageIteratorType = ImageRegionConstIterator<PriorsImageType>;
/** 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. */
using MembershipImageType = TInputVectorImage;
using MembershipPixelType = typename MembershipImageType::PixelType;
using MembershipImagePointer = typename MembershipImageType::Pointer;
using MembershipImageIteratorType = ImageRegionConstIterator<MembershipImageType>;
/** 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. */
using PosteriorsImageType = VectorImage<TPosteriorsPrecisionType, Self::Dimension>;
using PosteriorsPixelType = typename PosteriorsImageType::PixelType;
using PosteriorsImagePointer = typename PosteriorsImageType::Pointer;
using PosteriorsImageIteratorType = ImageRegionIterator<PosteriorsImageType>;
/** Decision rule to use for defining the label. */
using DecisionRuleType = Statistics::MaximumDecisionRule;
using DecisionRulePointer = DecisionRuleType::Pointer;
using typename Superclass::DataObjectPointer;
/** An image from a single component of the Posterior. */
using ExtractedComponentImageType = itk::Image<TPosteriorsPrecisionType, Self::Dimension>;
/** Optional Smoothing filter that will be applied to the Posteriors. */
using SmoothingFilterType = ImageToImageFilter<ExtractedComponentImageType, ExtractedComponentImageType>;
using SmoothingFilterPointer = typename SmoothingFilterType::Pointer;
/** Set/Get the smoothing filter that may optionally be applied to the
* posterior image. */
void
SetSmoothingFilter(SmoothingFilterType *);
itkGetConstMacro(SmoothingFilter, SmoothingFilterPointer);
/** Set the priors image. */
virtual void
SetPriors(const PriorsImageType *);
/** Number of iterations to apply the smoothing filter. */
itkSetMacro(NumberOfSmoothingIterations, unsigned int);
itkGetConstMacro(NumberOfSmoothingIterations, unsigned int);
/** This is overloaded to create the Posteriors output image. */
using DataObjectPointerArraySizeType = ProcessObject::DataObjectPointerArraySizeType;
using Superclass::MakeOutput;
DataObjectPointer
MakeOutput(DataObjectPointerArraySizeType idx) override;
#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();
~BayesianClassifierImageFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
void
GenerateData() override;
void
GenerateOutputInformation() override;
/** Compute the posteriors using the Bayes rule. If no priors are available,
* then the posteriors are just a copy of the memberships.
* Computes the labeled map for all combinations of conditions. */
virtual void
ComputeBayesRule();
/** Normalize the posteriors and smooth them using a user-provided. */
virtual void
NormalizeAndSmoothPosteriors();
/** Compute the labeled map based on the Maximum rule applied to the posteriors. */
virtual void
ClassifyBasedOnPosteriors();
/** Get the Posteriors Image. */
PosteriorsImageType *
GetPosteriorImage();
private:
bool m_UserProvidedPriors{ false };
bool m_UserProvidedSmoothingFilter{ false };
SmoothingFilterPointer m_SmoothingFilter{};
unsigned int m_NumberOfSmoothingIterations{ 0 };
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkBayesianClassifierImageFilter.hxx"
#endif
#endif
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