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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 itkScalarImageKmeansImageFilter_h
#define itkScalarImageKmeansImageFilter_h
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkSampleClassifierFilter.h"
#include "itkImageToListSampleAdaptor.h"
#include "itkMinimumDecisionRule.h"
#include "itkRegionOfInterestImageFilter.h"
#include <vector>
namespace itk
{
/** \class ScalarImageKmeansImageFilter
* \brief Classifies the intensity values of a scalar image using the K-Means algorithm.
*
* Given an input image with scalar values, it uses the K-Means statistical
* classifier in order to define labels for every pixel in the image. The
* filter is templated over the type of the input image. The output image is
* predefined as having the same dimension of the input image and pixel type
* unsigned char, under the assumption that the classifier will generate less
* than 256 classes.
*
* You may want to look also at the RelabelImageFilter that may be used as a
* postprocessing stage, in particular if you are interested in ordering the
* labels by their relative size in number of pixels.
*
* \sa Image
* \sa ImageKmeansModelEstimator
* \sa KdTreeBasedKmeansEstimator, WeightedCentroidKdTreeGenerator, KdTree
* \sa RelabelImageFilter
*
* \ingroup ClassificationFilters
* \ingroup ITKClassifiers
*
* \wiki
* \wikiexample{Statistics/ScalarImageKmeansImageFilter,Cluster the pixels in a greyscale image}
* \endwiki
*/
template< typename TInputImage,
typename TOutputImage = Image< unsigned char, TInputImage::ImageDimension > >
class ITK_TEMPLATE_EXPORT ScalarImageKmeansImageFilter:
public ImageToImageFilter< TInputImage, TOutputImage >
{
public:
/** Extract dimension from input and output image. */
itkStaticConstMacro(ImageDimension, unsigned int,
TInputImage::ImageDimension);
/** Convenient typedefs for simplifying declarations. */
typedef TInputImage InputImageType;
typedef TOutputImage OutputImageType;
/** Standard class typedefs. */
typedef ScalarImageKmeansImageFilter Self;
typedef ImageToImageFilter< InputImageType, OutputImageType > 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(ScalarImageKmeansImageFilter, ImageToImageFilter);
/** Image typedef support. */
typedef typename InputImageType::PixelType InputPixelType;
typedef typename OutputImageType::PixelType OutputPixelType;
/** Type used for representing the Mean values. */
typedef typename NumericTraits< InputPixelType >::RealType RealPixelType;
/** Create a List from the scalar image. */
typedef itk::Statistics::ImageToListSampleAdaptor< InputImageType > AdaptorType;
/** Define the Measurement vector type from the AdaptorType. */
typedef typename AdaptorType::MeasurementVectorType MeasurementVectorType;
typedef itk::Statistics::DistanceToCentroidMembershipFunction< MeasurementVectorType > MembershipFunctionType;
typedef itk::Statistics::SampleClassifierFilter< AdaptorType > ClassifierType;
typedef itk::Statistics::MinimumDecisionRule DecisionRuleType;
typedef typename ClassifierType::ClassLabelVectorType ClassLabelVectorType;
typedef typename ClassifierType::MembershipFunctionVectorType MembershipFunctionVectorType;
typedef typename MembershipFunctionType::CentroidType MembershipFunctionOriginType;
typedef typename MembershipFunctionType::Pointer MembershipFunctionPointer;
/** Create the K-d tree structure. */
typedef itk::Statistics::WeightedCentroidKdTreeGenerator< AdaptorType > TreeGeneratorType;
typedef typename TreeGeneratorType::KdTreeType TreeType;
typedef itk::Statistics::KdTreeBasedKmeansEstimator< TreeType > EstimatorType;
typedef typename EstimatorType::ParametersType ParametersType;
typedef typename InputImageType::RegionType ImageRegionType;
typedef RegionOfInterestImageFilter<
InputImageType,
InputImageType > RegionOfInterestFilterType;
/** Add a new class to the classification by specifying its initial mean. */
void AddClassWithInitialMean(RealPixelType mean);
/** Return the array of Means found after the classification. */
itkGetConstReferenceMacro(FinalMeans, ParametersType);
/** Set/Get the UseNonContiguousLabels flag. When this is set to false the
* labels are numbered contiguously, like in {0,1,3..N}. When the flag is set
* to true, the labels are selected in order to span the dynamic range of the
* output image. This last option is useful when the output image is intended
* only for display. The default value is false. */
itkSetMacro(UseNonContiguousLabels, bool);
itkGetConstReferenceMacro(UseNonContiguousLabels, bool);
itkBooleanMacro(UseNonContiguousLabels);
/** Set Region method to constrain classfication to a certain region */
void SetImageRegion(const ImageRegionType & region);
/** Get the region over which the statistics will be computed */
itkGetConstReferenceMacro(ImageRegion, ImageRegionType);
#ifdef ITK_USE_CONCEPT_CHECKING
// Begin concept checking
itkConceptMacro( InputHasNumericTraitsCheck,
( Concept::HasNumericTraits< InputPixelType > ) );
// End concept checking
#endif
protected:
ScalarImageKmeansImageFilter();
virtual ~ScalarImageKmeansImageFilter() ITK_OVERRIDE {}
void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
/** This method runs the statistical methods that identify the means of the
* classes and the use the distances to those means in order to label the
* image pixels.
* \sa ImageToImageFilter::GenerateData()
*/
void GenerateData() ITK_OVERRIDE;
/* See superclass for doxygen. This methods additionally checks that
* the number of means is not 0. */
virtual void VerifyPreconditions() ITK_OVERRIDE;
private:
ITK_DISALLOW_COPY_AND_ASSIGN(ScalarImageKmeansImageFilter);
typedef std::vector< RealPixelType > MeansContainer;
MeansContainer m_InitialMeans;
ParametersType m_FinalMeans;
bool m_UseNonContiguousLabels;
ImageRegionType m_ImageRegion;
bool m_ImageRegionDefined;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkScalarImageKmeansImageFilter.hxx"
#endif
#endif
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