ITK/Examples/Broken/Statistics/ExpectationMaximizationMixtureModelEstimator Image
From KitwarePublic
Jump to navigationJump to search
The clusters are not computed correctly.
ExpectationMaximizationMixtureModelEstimator_Image.cxx
<source lang="cpp">
- include "itkVector.h"
- include "itkListSample.h"
- include "itkGaussianMixtureModelComponent.h"
- include "itkExpectationMaximizationMixtureModelEstimator.h"
- include "itkSampleClassifierFilter.h"
- include "itkMaximumDecisionRule2.h"
- include "itkImageToListSampleFilter.h"
- include "itkCovariantVector.h"
- include "itkImageRegionIterator.h"
- include "itkImageFileReader.h"
- include "itkSimpleFilterWatcher.h"
typedef itk::CovariantVector<unsigned char, 3> PixelType; typedef itk::Image<PixelType, 2> ImageType;
static void ControlledImage(ImageType::Pointer image); static void RandomImage(ImageType::Pointer image);
int main(int argc, char*argv[]) {
ImageType::Pointer image = ImageType::New();
RandomImage(image); //ControlledImage(image);
typedef itk::Statistics::ImageToListSampleFilter<ImageType> ImageToListSampleFilterType; ImageToListSampleFilterType::Pointer imageToListSampleFilter = ImageToListSampleFilterType::New(); imageToListSampleFilter->SetInput(image); imageToListSampleFilter->Update();
unsigned int numberOfClasses = 3;
typedef itk::Array< double > ParametersType; ParametersType params( numberOfClasses + numberOfClasses*numberOfClasses ); // 3 for means and 9 for 3x3 covariance
// Create the first set (for the first cluster/model) of initial parameters
std::vector< ParametersType > initialParameters( numberOfClasses );
for(unsigned int i = 0; i < 3; i++)
{
params[i] = 5.0; // mean of dimension i
}
unsigned int counter = 0;
for(unsigned int i = 0; i < 3; i++)
{
for(unsigned int j = 0; j < 3; j++)
{
if(i == j)
{
params[3+counter] = 5; // diagonal
}
else
{
params[3+counter] = 0; // off-diagonal
}
counter++;
}
}
initialParameters[0] = params;
// Create the second set (for the second cluster/model) of initial parameters
params[0] = 210.0;
params[1] = 5.0;
params[2] = 5.0;
counter = 0;
for(unsigned int i = 0; i < 3; i++)
{
for(unsigned int j = 0; j < 3; j++)
{
if(i == j)
{
params[3+counter] = 5; // diagonal
}
else
{
params[3+counter] = 0; // off-diagonal
}
counter++;
}
}
initialParameters[1] = params;
// Create the third set (for the third cluster/model) of initial parameters
params[0] = 5.0;
params[1] = 210.0;
params[2] = 5.0;
counter = 0;
for(unsigned int i = 0; i < 3; i++)
{
for(unsigned int j = 0; j < 3; j++)
{
if(i == j)
{
params[3+counter] = 5; // diagonal
}
else
{
params[3+counter] = 0; // off-diagonal
}
counter++;
}
}
initialParameters[2] = params;
std::cout << "Initial parameters: " << std::endl;
for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
{
std::cout << initialParameters[i] << std::endl;
}
typedef itk::Statistics::GaussianMixtureModelComponent< ImageToListSampleFilterType::ListSampleType > ComponentType;
std::cout << "Number of samples: " << imageToListSampleFilter->GetOutput()->GetTotalFrequency() << std::endl;
// Create the components
std::vector< ComponentType::Pointer > components;
for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
{
components.push_back( ComponentType::New() );
(components[i])->SetSample( imageToListSampleFilter->GetOutput() );
(components[i])->SetParameters( initialParameters[i] );
}
typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator<
ImageToListSampleFilterType::ListSampleType > EstimatorType;
EstimatorType::Pointer estimator = EstimatorType::New();
estimator->SetSample( imageToListSampleFilter->GetOutput() ); estimator->SetMaximumIteration( 200 );
itk::Array< double > initialProportions(numberOfClasses); initialProportions[0] = 0.33; initialProportions[1] = 0.33; initialProportions[2] = 0.33;
std::cout << "Initial proportions: " << initialProportions << std::endl;
estimator->SetInitialProportions( initialProportions );
for ( unsigned int i = 0 ; i < numberOfClasses ; i++)
{
estimator->AddComponent( components[i]);
}
//itk::SimpleFilterWatcher watcher(estimator);
estimator->Update();
// Output the results
for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
{
std::cout << "Cluster[" << i << "]" << std::endl;
std::cout << " Parameters:" << std::endl;
std::cout << " " << (components[i])->GetFullParameters()
<< std::endl;
std::cout << " Proportion: ";
// Outputs: // mean of dimension 1, mean of dimension 2, covariance(0,0), covariance(0,1), covariance(1,0), covariance(1,1)
std::cout << " " << estimator->GetProportions()[i] << std::endl;
}
// Display the membership of each sample typedef itk::Statistics::SampleClassifierFilter< ImageToListSampleFilterType::ListSampleType > FilterType;
typedef itk::Statistics::MaximumDecisionRule2 DecisionRuleType; DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
typedef FilterType::ClassLabelVectorObjectType ClassLabelVectorObjectType; typedef FilterType::ClassLabelVectorType ClassLabelVectorType;
ClassLabelVectorObjectType::Pointer classLabelsObject = ClassLabelVectorObjectType::New(); ClassLabelVectorType & classLabelVector = classLabelsObject->Get();
typedef FilterType::ClassLabelType ClassLabelType;
ClassLabelType class0 = 0; classLabelVector.push_back( class0 );
ClassLabelType class1 = 1; classLabelVector.push_back( class1 );
ClassLabelType class2 = 2; classLabelVector.push_back( class2 );
FilterType::Pointer sampleClassifierFilter = FilterType::New(); sampleClassifierFilter->SetInput( imageToListSampleFilter->GetOutput() ); sampleClassifierFilter->SetNumberOfClasses( numberOfClasses ); sampleClassifierFilter->SetClassLabels( classLabelsObject ); sampleClassifierFilter->SetDecisionRule( decisionRule ); sampleClassifierFilter->SetMembershipFunctions( estimator->GetOutput() ); sampleClassifierFilter->Update();
const FilterType::MembershipSampleType* membershipSample = sampleClassifierFilter->GetOutput(); FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();
while ( iter != membershipSample->End() )
{
std::cout << (int)iter.GetMeasurementVector()[0] << " " << (int)iter.GetMeasurementVector()[1] << " " << (int)iter.GetMeasurementVector()[2]
<< " : " << iter.GetClassLabel() << std::endl;
++iter;
}
return EXIT_SUCCESS;
}
void ControlledImage(ImageType::Pointer image) {
// Create an image ImageType::RegionType region; ImageType::IndexType start; start[0] = 0; start[1] = 0;
ImageType::SizeType size; size[0] = 10; size[1] = 10;
region.SetSize(size); region.SetIndex(start);
image->SetRegions(region); image->Allocate();
// Make a red and a green square itk::CovariantVector<unsigned char, 3> green; green[0] = 0; green[1] = 255; green[2] = 0;
itk::CovariantVector<unsigned char, 3> red; red[0] = 255; red[1] = 0; red[2] = 0;
itk::CovariantVector<unsigned char, 3> black; black[0] = 0; black[1] = 0; black[2] = 0;
itk::ImageRegionIterator<ImageType> imageIterator(image,region); imageIterator.GoToBegin();
while(!imageIterator.IsAtEnd())
{
if(imageIterator.GetIndex()[0] > 2 && imageIterator.GetIndex()[0] < 5 &&
imageIterator.GetIndex()[1] > 2 && imageIterator.GetIndex()[1] < 5)
{
imageIterator.Set(green);
}
else if(imageIterator.GetIndex()[0] > 6 && imageIterator.GetIndex()[0] < 9 &&
imageIterator.GetIndex()[1] > 6 && imageIterator.GetIndex()[1] < 9)
{
imageIterator.Set(red);
}
else
{
imageIterator.Set(black);
}
++imageIterator;
}
}
void RandomImage(ImageType::Pointer image) {
// Create an image ImageType::RegionType region; ImageType::IndexType start; start[0] = 0; start[1] = 0;
ImageType::SizeType size; size[0] = 10; size[1] = 10;
region.SetSize(size); region.SetIndex(start);
image->SetRegions(region); image->Allocate();
itk::ImageRegionIterator<ImageType> imageIterator(image,region); imageIterator.GoToBegin();
while(!imageIterator.IsAtEnd())
{
// Get a random color
itk::CovariantVector<unsigned char, 3> pixel;
pixel[0] = rand() * 255;
pixel[1] = rand() * 255;
pixel[2] = rand() * 255;
imageIterator.Set(pixel);
++imageIterator;
}
} </source>
CMakeLists.txt
<syntaxhighlight lang="cmake"> cmake_minimum_required(VERSION 3.9.5)
project(ExpectationMaximizationMixtureModelEstimator Image)
find_package(ITK REQUIRED) include(${ITK_USE_FILE}) if (ITKVtkGlue_LOADED)
find_package(VTK REQUIRED)
include(${VTK_USE_FILE})
endif()
add_executable(ExpectationMaximizationMixtureModelEstimator Image MACOSX_BUNDLE ExpectationMaximizationMixtureModelEstimator Image.cxx)
if( "${ITK_VERSION_MAJOR}" LESS 4 )
target_link_libraries(ExpectationMaximizationMixtureModelEstimator Image ITKReview ${ITK_LIBRARIES})
else( "${ITK_VERSION_MAJOR}" LESS 4 )
target_link_libraries(ExpectationMaximizationMixtureModelEstimator Image ${ITK_LIBRARIES})
endif( "${ITK_VERSION_MAJOR}" LESS 4 )
</syntaxhighlight>
Download and Build ExpectationMaximizationMixtureModelEstimator Image
Click Image.tar here to download ExpectationMaximizationMixtureModelEstimator Image and its CMakeLists.txt file. Once the tarball ExpectationMaximizationMixtureModelEstimator Image.tar has been downloaded and extracted,
cd ExpectationMaximizationMixtureModelEstimator Image/build
- If ITK is installed:
cmake ..
- If ITK is not installed but compiled on your system, you will need to specify the path to your ITK build:
cmake -DITK_DIR:PATH=/home/me/itk_build ..
Build the project:
make
and run it:
./ExpectationMaximizationMixtureModelEstimator Image
WINDOWS USERS PLEASE NOTE: Be sure to add the ITK bin directory to your path. This will resolve the ITK dll's at run time.
Building All of the Examples
Many of the examples in the ITK Wiki Examples Collection require VTK. You can build all of the the examples by following these instructions. If you are a new VTK user, you may want to try the Superbuild which will build a proper ITK and VTK.
ItkVtkGlue
ITK >= 4
For examples that use QuickView (which depends on VTK), you must have built ITK with Module_ITKVtkGlue=ON.
ITK < 4
Some of the ITK Examples require VTK to display the images. If you download the entire ITK Wiki Examples Collection, the ItkVtkGlue directory will be included and configured. If you wish to just build a few examples, then you will need to download ItkVtkGlue and build it. When you run cmake it will ask you to specify the location of the ItkVtkGlue binary directory.
