Singlephase Chan and Vese Dense Field Level Set Segmentation#


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Single-phase Chan And Vese Dense Field Level Set Segmentation



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// The use of the ScalarChanAndVeseDenseLevelSetImageFilter is
// illustrated in the following example. The implementation of this filter in
// ITK is based on the paper by Chan And Vese.  This
// implementation extends the functionality of the
// level-set filters in ITK by using region-based variational techniques. These methods
// do not rely on the presence of edges in the images.
// ScalarChanAndVeseDenseLevelSetImageFilter expects two inputs.  The first is
// an initial level set in the form of an \doxygen{Image}. The second input
// is a feature image. For this algorithm, the feature image is the original
// raw or preprocessed image. Several parameters are required by the algorithm
// for regularization and weights of different energy terms. The user is encouraged to
// change different parameter settings to optimize the code example on their images.
// Let's start by including the headers of the main filters involved in the
// preprocessing.

#include "itkScalarChanAndVeseDenseLevelSetImageFilter.h"
#include "itkScalarChanAndVeseLevelSetFunctionData.h"
#include "itkConstrainedRegionBasedLevelSetFunctionSharedData.h"
#include "itkFastMarchingImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkImage.h"
#include "itkAtanRegularizedHeavisideStepFunction.h"

main(int argc, char ** argv)

  if (argc < 6)
    std::cerr << "Missing arguments" << std::endl;
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << " featureImage outputImage";
    std::cerr << " startx starty seedValue" << std::endl;
    return EXIT_FAILURE;

  unsigned int nb_iteration = 500;
  double       rms = 0.;
  double       epsilon = 1.;
  double       curvature_weight = 0.;
  double       area_weight = 0.;
  double       reinitialization_weight = 0.;
  double       volume_weight = 0.;
  double       volume = 0.;
  double       l1 = 1.;
  double       l2 = 1.;

  //  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.
  constexpr unsigned int Dimension = 2;
  using ScalarPixelType = float;
  using InternalImageType = itk::Image<ScalarPixelType, Dimension>;

  using DataHelperType = itk::ScalarChanAndVeseLevelSetFunctionData<InternalImageType, InternalImageType>;

  using SharedDataHelperType =
    itk::ConstrainedRegionBasedLevelSetFunctionSharedData<InternalImageType, InternalImageType, DataHelperType>;

  using LevelSetFunctionType =
    itk::ScalarChanAndVeseLevelSetFunction<InternalImageType, InternalImageType, SharedDataHelperType>;

  //  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
  using DomainFunctionType = itk::AtanRegularizedHeavisideStepFunction<ScalarPixelType, ScalarPixelType>;

  auto domainFunction = DomainFunctionType::New();

  InternalImageType::Pointer featureImage = itk::ReadImage<InternalImageType>(argv[1]);

  //  We declare now the type of the FastMarchingImageFilter that
  //  will be used to generate the initial level set in the form of a distance
  //  map.
  using FastMarchingFilterType = itk::FastMarchingImageFilter<InternalImageType, InternalImageType>;

  auto fastMarching = FastMarchingFilterType::New();

  //  The FastMarchingImageFilter requires the user to provide a seed
  //  point from which the level set will be generated. The user can actually
  //  pass not only one seed point but a set of them. Note the the
  //  FastMarchingImageFilter is used here only as a helper in the
  //  determination of an initial level set. We could have used the
  //  \doxygen{DanielssonDistanceMapImageFilter} in the same way.
  //  The seeds are passed stored in a container. The type of this
  //  container is defined as \code{NodeContainer} among the
  //  FastMarchingImageFilter traits.
  using NodeContainer = FastMarchingFilterType::NodeContainer;
  using NodeType = FastMarchingFilterType::NodeType;

  auto seeds = NodeContainer::New();

  InternalImageType::IndexType seedPosition;

  seedPosition[0] = std::stoi(argv[3]);
  seedPosition[1] = std::stoi(argv[4]);

  const double initialDistance = std::stod(argv[5]);

  NodeType node;

  const double seedValue = -initialDistance;


  //  The list of nodes is initialized and then every node is inserted using
  //  the \code{InsertElement()}.
  seeds->InsertElement(0, node);

  //  The set of seed nodes is passed now to the
  //  FastMarchingImageFilter with the method
  //  \code{SetTrialPoints()}.

  //  Since the FastMarchingImageFilter is used here just as a
  //  Distance Map generator. It does not require a speed image as input.
  //  Instead the constant value $1.0$ is passed using the
  //  \code{SetSpeedConstant()} method.

  //  The FastMarchingImageFilter requires the user to specify the
  //  size of the image to be produced as output. This is done using the
  //  \code{SetOutputSize()}. Note that the size is obtained here from the
  //  output image of the smoothing filter. The size of this image is valid
  //  only after the \code{Update()} methods of this filter has been called
  //  directly or indirectly.

  //  We declare now the type of the ScalarChanAndVeseDenseLevelSetImageFilter that
  //  will be used to generate a segmentation.

  using MultiLevelSetType = itk::ScalarChanAndVeseDenseLevelSetImageFilter<InternalImageType,

  auto levelSetFilter = MultiLevelSetType::New();

  //  We set the function count to 1 since a single level-set is being evolved.

  //  Set the feature image and initial level-set image as output of the
  //  fast marching image filter.
  levelSetFilter->SetLevelSet(0, fastMarching->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.

  //  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.

  //  For large images, we may want to compute the level-set over the initial supplied
  //  level-set image. This saves a lot of memory.

  //  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.


    itk::WriteImage(levelSetFilter->GetOutput(), argv[1]);
  catch (const itk::ExceptionObject & excep)
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
    return -1;

  return EXIT_SUCCESS;

Classes demonstrated#

template<typename TInputImage, typename TFeatureImage, typename TOutputImage, typename TFunction = ScalarChanAndVeseLevelSetFunction<TInputImage, TFeatureImage>, class TSharedData = typename TFunction::SharedDataType>
class ScalarChanAndVeseDenseLevelSetImageFilter : public itk::MultiphaseDenseFiniteDifferenceImageFilter<TInputImage, TFeatureImage, TOutputImage, TFunction>

Dense implementation of the Chan and Vese multiphase level set image filter.

This code was adapted from the paper:

   "An active contour model without edges"
    T. Chan and L. Vese.
    In Scale-Space Theories in Computer Vision, pages 141-151, 1999.

This code was taken from the Insight Journal paper:

"Cell Tracking using Coupled Active Surfaces for Nuclei and Membranes"

Mosaliganti K., Smith B., Gelas A., Gouaillard A., Megason S.

That is based on the papers:

"Level Set Segmentation: Active Contours without edge"


"Level set segmentation using coupled active surfaces"

See itk::ScalarChanAndVeseDenseLevelSetImageFilter for additional documentation.