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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.
*
*=========================================================================*/
/*=========================================================================
*
* Portions of this file are subject to the VTK Toolkit Version 3 copyright.
*
* Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
*
* For complete copyright, license and disclaimer of warranty information
* please refer to the NOTICE file at the top of the ITK source tree.
*
*=========================================================================*/
#ifndef itkBayesianClassifierImageFilter_hxx
#define itkBayesianClassifierImageFilter_hxx
#include "itkImageRegionConstIterator.h"
namespace itk
{
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
BayesianClassifierImageFilter()
: m_SmoothingFilter(nullptr)
{
this->SetNumberOfRequiredOutputs(2);
PosteriorsImagePointer p = static_cast<PosteriorsImageType *>(this->MakeOutput(1).GetPointer());
this->SetNthOutput(1, p.GetPointer());
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
GenerateData()
{
// Set up input image
const InputImageType * membershipImage = this->GetInput();
// Set up general parameters
const unsigned int numberOfClasses = membershipImage->GetVectorLength();
if (numberOfClasses == 0)
{
itkExceptionMacro("The number of components in the input Membership image is Zero !");
}
this->AllocateOutputs();
this->ComputeBayesRule();
if (m_UserProvidedSmoothingFilter)
{
this->NormalizeAndSmoothPosteriors();
}
this->ClassifyBasedOnPosteriors();
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
typename BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
PosteriorsImageType *
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
GetPosteriorImage()
{
auto * ptr = dynamic_cast<PosteriorsImageType *>(this->ProcessObject::GetOutput(1));
return ptr;
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
typename BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
DataObjectPointer
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
MakeOutput(DataObjectPointerArraySizeType idx)
{
if (idx == 1)
{
return PosteriorsImageType::New().GetPointer();
}
return Superclass::MakeOutput(idx);
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
GenerateOutputInformation()
{
Superclass::GenerateOutputInformation();
if (!this->GetPosteriorImage())
{
return;
}
// The vector length is part of the output information that must be
// updated here
this->GetPosteriorImage()->SetVectorLength(this->GetInput()->GetVectorLength());
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
ComputeBayesRule()
{
itkDebugMacro("Computing Bayes Rule");
const InputImageType * membershipImage = this->GetInput();
ImageRegionType imageRegion = membershipImage->GetBufferedRegion();
if (m_UserProvidedPriors)
{
const auto * priorsImage = dynamic_cast<const PriorsImageType *>(this->GetInput(1));
if (priorsImage == nullptr)
{
itkExceptionMacro("Second input type does not correspond to expected Priors Image Type");
}
PosteriorsImageType * posteriorsImage = this->GetPosteriorImage();
if (posteriorsImage == nullptr)
{
itkExceptionMacro("Second output type does not correspond to expected Posteriors Image Type");
}
InputImageIteratorType itrMembershipImage(membershipImage, imageRegion);
PriorsImageIteratorType itrPriorsImage(priorsImage, imageRegion);
PosteriorsImageIteratorType itrPosteriorsImage(posteriorsImage, imageRegion);
itrMembershipImage.GoToBegin();
itrPriorsImage.GoToBegin();
const unsigned int numberOfClasses = membershipImage->GetVectorLength();
itkDebugMacro("Computing Bayes Rule nclasses in membershipImage: " << numberOfClasses);
while (!itrMembershipImage.IsAtEnd())
{
PosteriorsPixelType posteriors(numberOfClasses);
const PriorsPixelType priors = itrPriorsImage.Get();
const MembershipPixelType memberships = itrMembershipImage.Get();
for (unsigned int i = 0; i < numberOfClasses; ++i)
{
posteriors[i] = static_cast<TPosteriorsPrecisionType>(memberships[i] * priors[i]);
}
itrPosteriorsImage.Set(posteriors);
++itrMembershipImage;
++itrPriorsImage;
++itrPosteriorsImage;
}
}
else
{
PosteriorsImageType * posteriorsImage = this->GetPosteriorImage();
if (posteriorsImage == nullptr)
{
itkExceptionMacro("Second output type does not correspond to expected Posteriors Image Type");
}
InputImageIteratorType itrMembershipImage(membershipImage, imageRegion);
PosteriorsImageIteratorType itrPosteriorsImage(posteriorsImage, imageRegion);
itrMembershipImage.GoToBegin();
itrPosteriorsImage.GoToBegin();
while (!itrMembershipImage.IsAtEnd())
{
itrPosteriorsImage.Set(itrMembershipImage.Get());
++itrMembershipImage;
++itrPosteriorsImage;
}
}
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
SetSmoothingFilter(SmoothingFilterType * smoothingFilter)
{
this->m_SmoothingFilter = smoothingFilter;
this->m_UserProvidedSmoothingFilter = true;
this->Modified();
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
SetPriors(const PriorsImageType * priors)
{
this->ProcessObject::SetNthInput(1, const_cast<PriorsImageType *>(priors));
this->m_UserProvidedPriors = true;
this->Modified();
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
NormalizeAndSmoothPosteriors()
{
PosteriorsImageIteratorType itrPosteriorImage(this->GetPosteriorImage(),
this->GetPosteriorImage()->GetBufferedRegion());
PosteriorsPixelType p;
const unsigned int numberOfClasses = this->GetPosteriorImage()->GetVectorLength();
for (unsigned int iter = 0; iter < m_NumberOfSmoothingIterations; ++iter)
{
itrPosteriorImage.GoToBegin();
while (!itrPosteriorImage.IsAtEnd())
{
p = itrPosteriorImage.Get();
// Normalize P so the probability across components sums to 1
TPosteriorsPrecisionType probability = 0;
for (unsigned int i = 0; i < numberOfClasses; ++i)
{
probability += p[i];
}
// Two approaches available:
// a) treat divide by zero as exception.
// b) consider norm({0, 0,...}) = 0,
// Option (b) was implemented
if (probability > 0)
{
p /= probability;
}
itrPosteriorImage.Set(p);
++itrPosteriorImage;
}
for (unsigned int componentToExtract = 0; componentToExtract < numberOfClasses; ++componentToExtract)
{
// Create an auxiliary image to store one component of the vector image.
// Smoothing filters typically can't handle multi-component images, so we
// will extract each component and smooth it.
auto extractedComponentImage = ExtractedComponentImageType::New();
extractedComponentImage->CopyInformation(this->GetPosteriorImage());
extractedComponentImage->SetBufferedRegion(this->GetPosteriorImage()->GetBufferedRegion());
extractedComponentImage->SetRequestedRegion(this->GetPosteriorImage()->GetRequestedRegion());
extractedComponentImage->Allocate();
using IteratorType = itk::ImageRegionIterator<ExtractedComponentImageType>;
itrPosteriorImage.GoToBegin();
IteratorType it(extractedComponentImage, extractedComponentImage->GetBufferedRegion());
it.GoToBegin();
while (!itrPosteriorImage.IsAtEnd())
{
it.Set(itrPosteriorImage.Get()[componentToExtract]);
++it;
++itrPosteriorImage;
}
m_SmoothingFilter->SetInput(extractedComponentImage);
m_SmoothingFilter->Modified(); // Force an update
m_SmoothingFilter->Update();
itrPosteriorImage.GoToBegin();
IteratorType sit(m_SmoothingFilter->GetOutput(), m_SmoothingFilter->GetOutput()->GetBufferedRegion());
sit.GoToBegin();
while (!itrPosteriorImage.IsAtEnd())
{
PosteriorsPixelType posteriorPixel = itrPosteriorImage.Get();
posteriorPixel[componentToExtract] = sit.Get();
itrPosteriorImage.Set(posteriorPixel);
++sit;
++itrPosteriorImage;
}
}
}
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
ClassifyBasedOnPosteriors()
{
OutputImagePointer labels = this->GetOutput();
ImageRegionType imageRegion = labels->GetBufferedRegion();
PosteriorsImageType * posteriorsImage = this->GetPosteriorImage();
if (posteriorsImage == nullptr)
{
itkExceptionMacro("Second output type does not correspond to expected Posteriors Image Type");
}
OutputImageIteratorType itrLabelsImage(labels, imageRegion);
PosteriorsImageIteratorType itrPosteriorsImage(posteriorsImage, imageRegion);
DecisionRulePointer decisionRule = DecisionRuleType::New();
itrLabelsImage.GoToBegin();
itrPosteriorsImage.GoToBegin();
typename PosteriorsImageType::PixelType posteriorsPixel;
typename DecisionRuleType::MembershipVectorType posteriorsVector;
posteriorsPixel = itrPosteriorsImage.Get();
posteriorsVector.reserve(posteriorsPixel.Size());
posteriorsVector.insert(posteriorsVector.begin(), posteriorsPixel.Size(), 0.0);
while (!itrLabelsImage.IsAtEnd())
{
posteriorsPixel = itrPosteriorsImage.Get();
std::copy_n(posteriorsPixel.GetDataPointer(), posteriorsPixel.Size(), posteriorsVector.begin());
itrLabelsImage.Set(static_cast<TLabelsType>(decisionRule->Evaluate(posteriorsVector)));
++itrLabelsImage;
++itrPosteriorsImage;
}
}
template <typename TInputVectorImage,
typename TLabelsType,
typename TPosteriorsPrecisionType,
typename TPriorsPrecisionType>
void
BayesianClassifierImageFilter<TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType>::
PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "UserProvidedPriors: " << (m_UserProvidedPriors ? "On" : "Off") << std::endl;
os << indent << "UserProvidedSmoothingFilter " << (m_UserProvidedSmoothingFilter ? "On" : "Off") << std::endl;
itkPrintSelfObjectMacro(SmoothingFilter);
os << indent << "NumberOfSmoothingIterations: " << m_NumberOfSmoothingIterations << std::endl;
}
} // end namespace itk
#endif
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