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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: ScalarImageKmeansModelEstimator.cxx
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.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainT1Slice.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example shows how to compute the KMeans model of an Scalar Image.
//
// The \subdoxygen{Statistics}{KdTreeBasedKmeansEstimator} is used for taking
// a scalar image and applying the K-Means algorithm in order to define classes
// that represents statistical distributions of intensity values in the pixels.
// In the context of Medical Imaging, each class is typically associated to a
// particular type of tissue and can therefore be used as a form of image
// segmentation. One of the drawbacks of this technique is that the spatial
// distribution of the pixels is not considered at all. It is common therefore
// to combine the classification resulting from K-Means with other segmentation
// techniques that will use the classification as a prior and add spatial
// information to it in order to produce a better segmentation.
//
// Software Guide : EndLatex
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkMinimumDecisionRule.h"
#include "itkEuclideanDistance.h"
#include "itkSampleClassifier.h"
#include "itkScalarImageToListAdaptor.h"
#include "itkImage.h"
#include "itkImageFileReader.h"
int main( int argc, char * argv [] )
{
if( argc < 2 )
{
std::cerr << "Missing command line arguments" << std::endl;
std::cerr << "Usage : " << argv[0] << " inputImageFileName " << std::endl;
return -1;
}
typedef unsigned char PixelType;
const unsigned int Dimension = 2;
typedef itk::Image<PixelType, Dimension > ImageType;
typedef itk::ImageFileReader< ImageType > ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
try
{
reader->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Problem encoutered while reading image file : " << argv[1] << std::endl;
std::cerr << excp << std::endl;
return -1;
}
// Software Guide : BeginCodeSnippet
// Create a List from the scalar image
typedef itk::Statistics::ScalarImageToListAdaptor< ImageType > AdaptorType;
AdaptorType::Pointer adaptor = AdaptorType::New();
adaptor->SetImage( reader->GetOutput() );
// Define the Measurement vector type from the AdaptorType
typedef AdaptorType::MeasurementVectorType MeasurementVectorType;
// Create the K-d tree structure
typedef itk::Statistics::WeightedCentroidKdTreeGenerator<
AdaptorType >
TreeGeneratorType;
TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New();
treeGenerator->SetSample( adaptor );
treeGenerator->SetBucketSize( 16 );
treeGenerator->Update();
typedef TreeGeneratorType::KdTreeType TreeType;
typedef itk::Statistics::KdTreeBasedKmeansEstimator<TreeType> EstimatorType;
EstimatorType::Pointer estimator = EstimatorType::New();
const unsigned int numberOfClasses = 3;
EstimatorType::ParametersType initialMeans( numberOfClasses );
initialMeans[0] = 25.0;
initialMeans[1] = 125.0;
initialMeans[2] = 250.0;
estimator->SetParameters( initialMeans );
estimator->SetKdTree( treeGenerator->GetOutput() );
estimator->SetMaximumIteration( 200 );
estimator->SetCentroidPositionChangesThreshold(0.0);
estimator->StartOptimization();
EstimatorType::ParametersType estimatedMeans = estimator->GetParameters();
for ( unsigned int i = 0 ; i < numberOfClasses ; ++i )
{
std::cout << "cluster[" << i << "] " << std::endl;
std::cout << " estimated mean : " << estimatedMeans[i] << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{BrainT1Slice.eps}
// \itkcaption[Output of the ScalarImageKmeansModelEstimator]{Test image for the
// KMeans model estimator.}
// \label{fig:ScalarImageKmeansModelEstimatorTestImage}
// \end{figure}
//
// The example produces means of 14.8, 91.6, 134.9 on
// Figure \ref{fig:ScalarImageKmeansModelEstimatorTestImage}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
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