[ITK] [ITK-users] Chan and Vese Segmentation for 3D data
Param Rajpura
Param.Rajpura at LntTechservices.com
Tue Feb 10 07:27:21 EST 2015
Hi all,
I am using ITK for segmentation from 3D MR Images data.
Initially applying ChanandVeseFilter on 2D images I could segment successfully but 3D data generates a problem.
I am using the same code provided in the example: "Single Phase Chan and Vese" the only difference is I am loading fast marching output instead of generating here.
I am unable to catch any exceptions here, but while debugging my code, first chance exception is thrown at:
1. Inside levelSetFilter->Update();
2. In ImportImageContainer.hxx
3. Line 181, data = new TElement[size];
Below is my code.
typedef itk::ImageFileReader< MainType > ReaderType;
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName("E:\\Intermediate Results\\Sample001_MRI.mhd");
try
{
reader->Update();
}
catch (itk::ExceptionObject & excep)
{
std::cerr << "Exception caught in read !" << std::endl;
std::cerr << excep << std::endl;
return -1;
}
typedef itk::CastImageFilter<MainType, InternalImageType> FloatConversionFilter;
FloatConversionFilter::Pointer ImgTypeConverter = FloatConversionFilter::New();
ImgTypeConverter->SetInput(reader->GetOutput());
ImgTypeConverter->Update();
typedef itk::ImageFileWriter< InternalImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
writer->SetFileName("E:\\Intermediate Results\\Out.mhd");
typedef itk::ImageFileReader< InternalImageType > ReaderType1;
ReaderType1::Pointer reader1 = ReaderType1::New();
reader1->SetFileName("E:\\Intermediate Results\\FastMarching.mhd");
reader1->Update();
InternalImageType::Pointer Image = InternalImageType::New();
Image = ImgTypeConverter->GetOutput();
Image->Print(std::cout);
typedef itk::ChangeInformationImageFilter<InternalImageType> ChangeFilter;
ChangeFilter::Pointer change = ChangeFilter::New();
change->ChangeAll();
change->UseReferenceImageOn();
change->SetReferenceImage(Image);
change->SetInput(reader1->GetOutput());
try
{
change->Update();
}
catch (itk::ExceptionObject & excep)
{
std::cerr << "Exception caught in change !" << std::endl;
std::cerr << excep << std::endl;
return -1;
}
Image = change->GetOutput();
Image->Print(std::cout);
unsigned int nb_iteration = 1;
double rms = 0.;
double epsilon = 2.;
double curvature_weight = 0.;
double area_weight = 0.;
double reinitialization_weight = 0.;
double volume_weight = 0.5;
double volume = 0.5;
double l1 = 1.;
double l2 = 1.;
double overlap_weight = 100.0;
//
// We now define the image type using a particular pixel type and
// dimension. In this case the \code{float} type is used for the pixels
// due to the requirements of the smoothing filter.
//
const unsigned int Dimension = 3;
typedef float ScalarPixelType;
typedef itk::Image< ScalarPixelType, Dimension > InternalImageType;
typedef itk::Image< unsigned short, Dimension > MainType;
typedef itk::ScalarChanAndVeseLevelSetFunctionData< InternalImageType,
InternalImageType > DataHelperType;
typedef itk::ConstrainedRegionBasedLevelSetFunctionSharedData<
InternalImageType, InternalImageType, DataHelperType > SharedDataHelperType;
typedef itk::ScalarChanAndVeseLevelSetFunction< InternalImageType,
InternalImageType, SharedDataHelperType > LevelSetFunctionType;
// We declare now the type of the numerically discretized Step and Delta functions that
// will be used in the level-set computations for foreground and background regions
//
typedef itk::AtanRegularizedHeavisideStepFunction< ScalarPixelType,
ScalarPixelType > DomainFunctionType;
DomainFunctionType::Pointer domainFunction = DomainFunctionType::New();
domainFunction->SetEpsilon(epsilon);
typedef itk::ScalarChanAndVeseSparseLevelSetImageFilter< InternalImageType,
InternalImageType, InternalImageType, LevelSetFunctionType,
SharedDataHelperType > MultiLevelSetType;
MultiLevelSetType::Pointer levelSetFilter = MultiLevelSetType::New();
// We set the function count to 1 since a single level-set is being evolved.
//
levelSetFilter->SetFunctionCount(1);
// Set the feature image and initial level-set image as output of the
// fast marching image filter.
//
levelSetFilter->SetFeatureImage(ImgTypeConverter->GetOutput());
levelSetFilter->SetLevelSet(0, change->GetOutput());
//levelSetFilter->SetLevelSet(1, fastMarching1->GetOutput());
// Once activiated the level set evolution will stop if the convergence
// criteria or if the maximum number of iterations is reached. The
// convergence criteria is defined in terms of the root mean squared (RMS)
// change in the level set function. The evolution is said to have
// converged if the RMS change is below a user specified threshold. In a
// real application is desirable to couple the evolution of the zero set
// to a visualization module allowing the user to follow the evolution of
// the zero set. With this feedback, the user may decide when to stop the
// algorithm before the zero set leaks through the regions of low gradient
// in the contour of the anatomical structure to be segmented.
//
levelSetFilter->SetNumberOfIterations(nb_iteration);
levelSetFilter->SetMaximumRMSError(rms);
// Often, in real applications, images have different pixel resolutions. In such
// cases, it is best to use the native spacings to compute derivatives etc rather
// than sampling the images.
//
//levelSetFilter->SetUseImageSpacing(1);
// For large images, we may want to compute the level-set over the initial supplied
// level-set image. This saves a lot of memory.
//
levelSetFilter->SetInPlace(false);
// For the level set with phase 0, set different parameters and weights. This may
// to be set in a loop for the case of multiple level-sets evolving simultaneously.
//
for (unsigned int i = 0; i < 1; i++)
{
levelSetFilter->GetDifferenceFunction(i)->SetDomainFunction(domainFunction);
levelSetFilter->GetDifferenceFunction(i)->SetCurvatureWeight(curvature_weight);
levelSetFilter->GetDifferenceFunction(i)->SetAreaWeight(area_weight);
//levelSetFilter->GetDifferenceFunction(i)->SetOverlapPenaltyWeight(overlap_weight);
levelSetFilter->GetDifferenceFunction(i)->SetVolumeMatchingWeight(volume_weight);
levelSetFilter->GetDifferenceFunction(i)->SetVolume(volume);
levelSetFilter->GetDifferenceFunction(i)->SetLambda1(l1);
levelSetFilter->GetDifferenceFunction(i)->SetLambda2(l2);
}
try
{
levelSetFilter->Update();
}
catch (itk::ExceptionObject & excep)
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
return -1;
}
catch (...)
{
std::cerr << "Exception Caught!!!!!" << std::endl;
return -1;
}
Image = levelSetFilter->GetOutput();
Image->Print(std::cout);
writer->SetInput(levelSetFilter->GetOutput());
writer->Update();
return EXIT_SUCCESS;
}
Also I would like to know what difference it makes when I use 3D data for fast marching. By looking at the distance map it doesn't look like a shere centered at the given seed point in 3D.
And what data should ideally be passed to ChanVese from FastMarching for 3D dataset.
Pls help!
Param
L&T Technology Services Ltd
www.LntTechservices.com<http://www.lnttechservices.com/>
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