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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkScalarImageKmeansImageFilter.h,v $
Language: C++
Date: $Date: 2006-04-05 13:59:37 $
Version: $Revision: 1.4 $
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 __itkScalarImageKmeansImageFilter_h
#define __itkScalarImageKmeansImageFilter_h
#include "itkImageToImageFilter.h"
#include "itkImage.h"
#include "itkNumericTraits.h"
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkMinimumDecisionRule.h"
#include "itkEuclideanDistance.h"
#include "itkSampleClassifier.h"
#include "itkScalarImageToListAdaptor.h"
#include "itkImageRegion.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
*/
template <class TInputImage >
class ITK_EXPORT ScalarImageKmeansImageFilter :
public ImageToImageFilter< TInputImage, Image<unsigned char,
::itk::GetImageDimension<TInputImage>::ImageDimension> >
{
public:
/** Extract dimension from input and output image. */
itkStaticConstMacro(ImageDimension, unsigned int,
TInputImage::ImageDimension);
/** Convenient typedefs for simplifying declarations. */
typedef TInputImage InputImageType;
typedef Image<unsigned char,
::itk::GetImageDimension<TInputImage>::ImageDimension> 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::ScalarImageToListAdaptor<
InputImageType > AdaptorType;
/** Define the Measurement vector type from the AdaptorType */
typedef typename AdaptorType::MeasurementVectorType MeasurementVectorType;
/** 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() {}
void PrintSelf(std::ostream& os, Indent indent) const;
/** 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();
private:
ScalarImageKmeansImageFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
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.txx"
#endif
#endif
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