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| The clusters are not computed correctly.
| | {{warning|1=The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.}} |
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| ==ExpectationMaximizationMixtureModelEstimator_Image.cxx==
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| <source lang="cpp">
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| #include "itkVector.h"
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| #include "itkListSample.h"
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| #include "itkGaussianMixtureModelComponent.h"
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| #include "itkExpectationMaximizationMixtureModelEstimator.h"
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| #include "itkSampleClassifierFilter.h"
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| #include "itkMaximumDecisionRule2.h"
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| #include "itkImageToListSampleFilter.h"
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| #include "itkCovariantVector.h"
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| #include "itkImageRegionIterator.h"
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| #include "itkImageFileReader.h"
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| #include "itkSimpleFilterWatcher.h"
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| | |
| typedef itk::CovariantVector<unsigned char, 3> PixelType;
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| typedef itk::Image<PixelType, 2> ImageType;
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| | |
| void ControlledImage(ImageType::Pointer image);
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| void RandomImage(ImageType::Pointer image);
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| int main(int argc, char*argv[])
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| { | |
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| ImageType::Pointer image = ImageType::New();
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| RandomImage(image);
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| //ControlledImage(image);
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| typedef itk::Statistics::ImageToListSampleFilter<ImageType> ImageToListSampleFilterType;
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| ImageToListSampleFilterType::Pointer imageToListSampleFilter = ImageToListSampleFilterType::New();
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| imageToListSampleFilter->SetInput(image);
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| imageToListSampleFilter->Update();
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| | |
| unsigned int numberOfClasses = 3;
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| | |
| typedef itk::Array< double > ParametersType;
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| ParametersType params( numberOfClasses + numberOfClasses*numberOfClasses ); // 3 for means and 9 for 3x3 covariance
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| | |
| // Create the first set (for the first cluster/model) of initial parameters
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| std::vector< ParametersType > initialParameters( numberOfClasses );
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| for(unsigned int i = 0; i < 3; i++)
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| {
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| params[i] = 5.0; // mean of dimension i
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| }
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| unsigned int counter = 0;
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| for(unsigned int i = 0; i < 3; i++)
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| {
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| for(unsigned int j = 0; j < 3; j++)
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| {
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| if(i == j)
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| {
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| params[3+counter] = 5; // diagonal
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| }
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| else
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| {
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| params[3+counter] = 0; // off-diagonal
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| }
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| counter++;
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| }
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| }
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| | |
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| initialParameters[0] = params;
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| // Create the second set (for the second cluster/model) of initial parameters
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| params[0] = 210.0;
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| params[1] = 5.0;
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| params[2] = 5.0;
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| counter = 0;
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| for(unsigned int i = 0; i < 3; i++)
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| {
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| for(unsigned int j = 0; j < 3; j++)
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| {
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| if(i == j)
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| {
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| params[3+counter] = 5; // diagonal
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| }
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| else
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| {
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| params[3+counter] = 0; // off-diagonal
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| }
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| counter++;
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| }
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| }
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| initialParameters[1] = params;
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| | |
| // Create the third set (for the third cluster/model) of initial parameters
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| params[0] = 5.0;
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| params[1] = 210.0;
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| params[2] = 5.0;
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| counter = 0;
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| for(unsigned int i = 0; i < 3; i++)
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| {
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| for(unsigned int j = 0; j < 3; j++)
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| {
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| if(i == j)
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| {
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| params[3+counter] = 5; // diagonal
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| }
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| else
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| {
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| params[3+counter] = 0; // off-diagonal
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| }
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| counter++;
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| }
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| }
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| initialParameters[2] = params;
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| | |
| std::cout << "Initial parameters: " << std::endl;
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| for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
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| {
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| std::cout << initialParameters[i] << std::endl;
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| }
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| | |
| typedef itk::Statistics::GaussianMixtureModelComponent< ImageToListSampleFilterType::ListSampleType >
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| ComponentType;
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| | |
| std::cout << "Number of samples: " << imageToListSampleFilter->GetOutput()->GetTotalFrequency() << std::endl;
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| | |
| // Create the components
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| std::vector< ComponentType::Pointer > components;
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| for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
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| {
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| components.push_back( ComponentType::New() );
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| (components[i])->SetSample( imageToListSampleFilter->GetOutput() );
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| (components[i])->SetParameters( initialParameters[i] );
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| }
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|
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| | |
| typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator<
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| ImageToListSampleFilterType::ListSampleType > EstimatorType;
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| EstimatorType::Pointer estimator = EstimatorType::New();
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| estimator->SetSample( imageToListSampleFilter->GetOutput() );
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| estimator->SetMaximumIteration( 200 );
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| | |
| itk::Array< double > initialProportions(numberOfClasses);
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| initialProportions[0] = 0.33;
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| initialProportions[1] = 0.33;
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| initialProportions[2] = 0.33;
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| std::cout << "Initial proportions: " << initialProportions << std::endl;
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| estimator->SetInitialProportions( initialProportions );
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| | |
| for ( unsigned int i = 0 ; i < numberOfClasses ; i++)
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| {
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| estimator->AddComponent( components[i]);
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| }
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| //itk::SimpleFilterWatcher watcher(estimator);
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| estimator->Update();
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| | |
| // Output the results
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| for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
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| {
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| std::cout << "Cluster[" << i << "]" << std::endl;
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| std::cout << " Parameters:" << std::endl;
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| std::cout << " " << (components[i])->GetFullParameters()
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| << std::endl;
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| std::cout << " Proportion: ";
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| // Outputs: // mean of dimension 1, mean of dimension 2, covariance(0,0), covariance(0,1), covariance(1,0), covariance(1,1)
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| std::cout << " " << estimator->GetProportions()[i] << std::endl;
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| }
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| | |
| // Display the membership of each sample
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| typedef itk::Statistics::SampleClassifierFilter< ImageToListSampleFilterType::ListSampleType > FilterType;
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| | |
| typedef itk::Statistics::MaximumDecisionRule2 DecisionRuleType;
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| DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
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| | |
| typedef FilterType::ClassLabelVectorObjectType ClassLabelVectorObjectType;
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| typedef FilterType::ClassLabelVectorType ClassLabelVectorType;
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| | |
| ClassLabelVectorObjectType::Pointer classLabelsObject = ClassLabelVectorObjectType::New();
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| ClassLabelVectorType & classLabelVector = classLabelsObject->Get();
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| typedef FilterType::ClassLabelType ClassLabelType;
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| ClassLabelType class0 = 0;
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| classLabelVector.push_back( class0 );
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| ClassLabelType class1 = 1;
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| classLabelVector.push_back( class1 );
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| ClassLabelType class2 = 2;
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| classLabelVector.push_back( class2 );
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| | |
| FilterType::Pointer sampleClassifierFilter = FilterType::New();
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| sampleClassifierFilter->SetInput( imageToListSampleFilter->GetOutput() );
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| sampleClassifierFilter->SetNumberOfClasses( numberOfClasses );
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| sampleClassifierFilter->SetClassLabels( classLabelsObject );
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| sampleClassifierFilter->SetDecisionRule( decisionRule );
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| sampleClassifierFilter->SetMembershipFunctions( estimator->GetOutput() );
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| sampleClassifierFilter->Update(); | |
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| const FilterType::MembershipSampleType* membershipSample = sampleClassifierFilter->GetOutput();
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| FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();
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| | |
| while ( iter != membershipSample->End() )
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| {
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| std::cout << (int)iter.GetMeasurementVector()[0] << " " << (int)iter.GetMeasurementVector()[1] << " " << (int)iter.GetMeasurementVector()[2]
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| << " : " << iter.GetClassLabel() << std::endl;
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| ++iter;
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| }
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| return EXIT_SUCCESS;
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| }
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| | |
| void ControlledImage(ImageType::Pointer image)
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| {
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| // Create an image
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| ImageType::RegionType region;
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| ImageType::IndexType start;
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| start[0] = 0;
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| start[1] = 0;
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| | |
| ImageType::SizeType size;
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| size[0] = 10;
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| size[1] = 10;
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| region.SetSize(size);
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| region.SetIndex(start);
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| image->SetRegions(region);
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| image->Allocate();
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| | |
| // Make a red and a green square
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| itk::CovariantVector<unsigned char, 3> green;
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| green[0] = 0;
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| green[1] = 255;
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| green[2] = 0;
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| | |
| itk::CovariantVector<unsigned char, 3> red;
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| red[0] = 255;
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| red[1] = 0;
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| red[2] = 0;
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| | |
| itk::CovariantVector<unsigned char, 3> black;
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| black[0] = 0;
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| black[1] = 0;
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| black[2] = 0;
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| | |
| itk::ImageRegionIterator<ImageType> imageIterator(image,region);
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| imageIterator.GoToBegin();
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| while(!imageIterator.IsAtEnd())
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| {
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| if(imageIterator.GetIndex()[0] > 2 && imageIterator.GetIndex()[0] < 5 &&
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| imageIterator.GetIndex()[1] > 2 && imageIterator.GetIndex()[1] < 5)
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| {
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| imageIterator.Set(green);
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| }
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| else if(imageIterator.GetIndex()[0] > 6 && imageIterator.GetIndex()[0] < 9 &&
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| imageIterator.GetIndex()[1] > 6 && imageIterator.GetIndex()[1] < 9)
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| {
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| imageIterator.Set(red);
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| }
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| else
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| {
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| imageIterator.Set(black);
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| }
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| ++imageIterator;
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| }
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| } | |
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| void RandomImage(ImageType::Pointer image)
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| {
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| // Create an image
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| ImageType::RegionType region;
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| ImageType::IndexType start;
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| start[0] = 0;
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| start[1] = 0;
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| ImageType::SizeType size;
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| size[0] = 10;
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| size[1] = 10;
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| region.SetSize(size);
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| region.SetIndex(start);
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| image->SetRegions(region);
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| image->Allocate();
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| itk::ImageRegionIterator<ImageType> imageIterator(image,region);
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| imageIterator.GoToBegin();
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| while(!imageIterator.IsAtEnd())
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| {
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| // Get a random color
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| itk::CovariantVector<unsigned char, 3> pixel;
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| pixel[0] = rand() * 255;
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| pixel[1] = rand() * 255;
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| pixel[2] = rand() * 255;
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| imageIterator.Set(pixel);
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| ++imageIterator;
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| }
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| | |
| }
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| </source>
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| | |
| ==CMakeLists.txt==
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| <source lang="cmake">
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| cmake_minimum_required(VERSION 2.6)
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| PROJECT(ExpectationMaximizationMixtureModelEstimator)
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| FIND_PACKAGE(ITK REQUIRED)
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| INCLUDE(${ITK_USE_FILE})
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| ADD_EXECUTABLE(ExpectationMaximizationMixtureModelEstimator_Image ExpectationMaximizationMixtureModelEstimator_Image.cxx)
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| TARGET_LINK_LIBRARIES(ExpectationMaximizationMixtureModelEstimator_Image
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| ITKBasicFilters ITKCommon ITKIO ITKStatistics)
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| | |
| </source>
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