[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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