Examples/Segmentation/ThresholdSegmentationLevelSetImageFilter.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {ThresholdSegmentationLevelSetImageFilterWhiteMatter.png}
//    ARGUMENTS:    60 116 5 150 180
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {ThresholdSegmentationLevelSetImageFilterVentricle.png}
//    ARGUMENTS:    81 112 5 210 250
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {ThresholdSegmentationLevelSetImageFilterGrayMatter.png}
//    ARGUMENTS:    107 69 5 180  210
//  Software Guide : EndCommandLineArgs
 
// Software Guide : BeginLatex
//
// \index{itk::Threshold\-Segmentation\-Level\-Set\-Image\-Filter}
//
// The \doxygen{ThresholdSegmentationLevelSetImageFilter} is an extension of
// the threshold connected-component segmentation to the level set framework.
// The goal is to define a range of intensity values that classify the tissue
// type of interest and then base the propagation term on the level set
// equation for that intensity range.  Using the level set approach, the
// smoothness of the evolving surface can be constrained to prevent some of
// the ``leaking'' that is common in connected-component schemes.
//
// The propagation term $P$ from Equation~\ref{eqn:LevelSetEquation} is
// calculated from the \code{FeatureImage} input $g$ with
// \code{UpperThreshold} $U$ and \code{LowerThreshold} $L$ according to the
// following formula.
//
// \begin{equation}
// \label{eqn:ThresholdSegmentationLevelSetImageFilterPropagationTerm}
// P(\mathbf{x}) = \left\{ \begin{array}{ll} g(\mathbf{x}) - L &
// \mbox{if $g(\mathbf{x}) < (U-L)/2 + L$} \\ U - g(\mathbf{x}) &
// \mbox{otherwise} \end{array} \right.  \end{equation}
//
// Figure~\ref{fig:ThresholdSegmentationSpeedTerm} illustrates the propagation
// term function.  Intensity values in $g$ between $L$ and $H$ yield positive
// values in $P$, while outside intensities yield negative values in $P$.
//
// \begin{figure} \center
// \includegraphics[width=0.8\textwidth]{ThresholdSegmentationLevelSetImageFilterCollaborationDiagram1}
// \itkcaption[ThresholdSegmentationLevelSetImageFilter collaboration
// diagram]{Collaboration diagram for the
// ThresholdSegmentationLevelSetImageFilter applied to a segmentation task.}
// \label{fig:ThresholdSegmentationLevelSetImageFilterDiagram}
// \end{figure}
//
// \begin{figure} \center
// \includegraphics[width=6.5cm]{ThresholdSegmentationLevelSetImageFilterFigure1}
// \itkcaption[Propagation term for threshold-based level set segmentation]
// {Propagation term for threshold-based level set segmentation.
// From
// Equation~\ref{eqn:ThresholdSegmentationLevelSetImageFilterPropagationTerm}.
// \label{fig:ThresholdSegmentationSpeedTerm}}
// \end{figure}
//
// The threshold segmentation filter expects two inputs.  The first is an
// initial level set in the form of an \doxygen{Image}. The second input is
// the feature image $g$.  For many applications, this filter requires little
// or no preprocessing of its input.  Smoothing the input image is not
// usually required to produce reasonable solutions, though it may still be
// warranted in some cases.
//
// Figure~\ref{fig:ThresholdSegmentationLevelSetImageFilterDiagram} shows how
// the image processing pipeline is constructed. The initial surface is
// generated using the fast marching filter.  The output of the segmentation
// filter is passed to a \doxygen{BinaryThresholdImageFilter} to create a
// binary representation of the segmented object.  Let's start by including
// the appropriate header file.
//
// Software Guide : EndLatex
 
 
#include "itkImage.h"
// Software Guide : BeginCodeSnippet
#include "itkThresholdSegmentationLevelSetImageFilter.h"
// Software Guide : EndCodeSnippet
#include "itkFastMarchingImageFilter.h"
#include "itkBinaryThresholdImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkZeroCrossingImageFilter.h"
 
 
int
main(int argc, char * argv[])
{
  if (argc < 8)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImage  outputImage";
    std::cerr << " seedX seedY InitialDistance";
    std::cerr << " LowerThreshold";
    std::cerr << " UpperThreshold";
    std::cerr << " [CurvatureScaling == 1.0]";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }
 
  //  Software Guide : BeginLatex
  //
  //  We define the image type using a particular pixel type and
  //  dimension. In this case we will use 2D \code{float} images.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InternalPixelType = float;
  constexpr unsigned int Dimension = 2;
  using InternalImageType = itk::Image<InternalPixelType, Dimension>;
  // Software Guide : EndCodeSnippet
 
  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
  using ThresholdingFilterType =
    itk::BinaryThresholdImageFilter<InternalImageType, OutputImageType>;
 
  ThresholdingFilterType::Pointer thresholder = ThresholdingFilterType::New();
 
  thresholder->SetLowerThreshold(-1000.0);
  thresholder->SetUpperThreshold(0.0);
 
  thresholder->SetOutsideValue(0);
  thresholder->SetInsideValue(255);
 
  using ReaderType = itk::ImageFileReader<InternalImageType>;
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  ReaderType::Pointer reader = ReaderType::New();
  WriterType::Pointer writer = WriterType::New();
 
  reader->SetFileName(argv[1]);
  writer->SetFileName(argv[2]);
 
 
  //  We now declare the type of the \doxygen{FastMarchingImageFilter} that
  //  will be used to generate the initial level set in the form of a distance
  //  map.
  //
  using FastMarchingFilterType =
    itk::FastMarchingImageFilter<InternalImageType, InternalImageType>;
 
  FastMarchingFilterType::Pointer fastMarching =
    FastMarchingFilterType::New();
 
  //  Software Guide : BeginLatex
  //
  //  The following lines instantiate a
  //  ThresholdSegmentationLevelSetImageFilter using the \code{New()} method.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using ThresholdSegmentationLevelSetImageFilterType =
    itk::ThresholdSegmentationLevelSetImageFilter<InternalImageType,
                                                  InternalImageType>;
  ThresholdSegmentationLevelSetImageFilterType::Pointer
    thresholdSegmentation =
      ThresholdSegmentationLevelSetImageFilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  For the ThresholdSegmentationLevelSetImageFilter, scaling
  //  parameters are used to balance the influence of the propagation
  //  (inflation) and the curvature (surface smoothing) terms from
  //  Equation~\ref{eqn:LevelSetEquation}. The advection term is not used in
  //  this filter. Set the terms with methods \code{SetPropagationScaling()}
  //  and \code{SetCurvatureScaling()}. Both terms are set to 1.0 in this
  //  example.
  //
  //  \index{itk::Threshold\-Segmentation\-Level\-Set\-Image\-Filter!SetPropagationScaling()}
  //  \index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetPropagationScaling()}
  //  \index{itk::Threshold\-Segmentation\-Level\-Set\-Image\-Filter!SetCurvatureScaling()}
  //  \index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetCurvatureScaling()}
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  thresholdSegmentation->SetPropagationScaling(1.0);
  if (argc > 8)
  {
    thresholdSegmentation->SetCurvatureScaling(std::stod(argv[8]));
  }
  else
  {
    thresholdSegmentation->SetCurvatureScaling(1.0);
  }
 
  //  Software Guide : EndCodeSnippet
 
  //  The level set solver will stop if the convergence criteria has been
  //  reached or if the maximum number of iterations has elasped.  The
  //  convergence criteria is defined in terms of the root mean squared (RMS)
  //  change in the level set function. When RMS change for an iteration is
  //  below a user-specified threshold, the solution is considered to have
  //  converged.
  thresholdSegmentation->SetMaximumRMSError(0.02);
  thresholdSegmentation->SetNumberOfIterations(1200);
 
  //    thresholdSegmentation->SetMaximumRMSError( std::stod(argv[8]) );
  //    thresholdSegmentation->SetNumberOfIterations( std::stoi(argv[9]) );
 
  // Software Guide : BeginLatex
  //
  // The convergence criteria \code{MaximumRMSError} and
  // \code{MaximumIterations} are set as in previous examples.  We now set
  // the upper and lower threshold values $U$ and $L$, and the isosurface
  // value to use in the initial model.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  thresholdSegmentation->SetUpperThreshold(::std::stod(argv[7]));
  thresholdSegmentation->SetLowerThreshold(::std::stod(argv[6]));
  thresholdSegmentation->SetIsoSurfaceValue(0.0);
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  The filters are now connected in a pipeline indicated in
  //  Figure~\ref{fig:ThresholdSegmentationLevelSetImageFilterDiagram}.
  //  Remember that before calling \code{Update()} on the file writer object,
  //  the fast marching filter must be initialized with the seed points and
  //  the output from the reader object.  See previous examples and the
  //  source code for this section for details.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  thresholdSegmentation->SetInput(fastMarching->GetOutput());
  thresholdSegmentation->SetFeatureImage(reader->GetOutput());
  thresholder->SetInput(thresholdSegmentation->GetOutput());
  writer->SetInput(thresholder->GetOutput());
  // Software Guide : EndCodeSnippet
 
  //
  //  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;
 
  NodeContainer::Pointer seeds = NodeContainer::New();
 
  InternalImageType::IndexType seedPosition;
 
  seedPosition[0] = std::stoi(argv[3]);
  seedPosition[1] = std::stoi(argv[4]);
 
  //  Nodes are created as stack variables and initialized with a value and an
  //  \doxygen{Index} position. Note that here we assign the value of minus
  //  the user-provided distance to the unique node of the seeds passed to the
  //  FastMarchingImageFilter. In this way, the value will increment
  //  as the front is propagated, until it reaches the zero value
  //  corresponding to the contour. After this, the front will continue
  //  propagating until it fills up the entire image. The initial distance is
  //  taken here from the command line arguments. The rule of thumb for the
  //  user is to select this value as the distance from the seed points at
  //  which he want the initial contour to be.
 
  const double initialDistance = std::stod(argv[5]);
 
  NodeType node;
 
  const double seedValue = -initialDistance;
 
  node.SetValue(seedValue);
  node.SetIndex(seedPosition);
 
  //
  //  The list of nodes is initialized and then every node is inserted using
  //  the \code{InsertElement()}.
  seeds->Initialize();
  seeds->InsertElement(0, node);
 
 
  //  The set of seed nodes is passed now to the
  //  FastMarchingImageFilter with the method
  //  \code{SetTrialPoints()}.
  fastMarching->SetTrialPoints(seeds);
 
  //
  //  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.
 
  fastMarching->SetSpeedConstant(1.0);
 
 
  //  The FastMarchingImageFilter requires the user to specify the size of the
  //  image to be produced as output. This is done using the
  //  \code{SetOutputRegion()} method. 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. Other image parameters such as Origin,
  //  Spacing and Direction are set in a similar manner.
 
 
  //  Software Guide : BeginLatex
  //
  //  Invoking the \code{Update()} method on the writer triggers the
  //  execution of the pipeline.  As usual, the call is placed in a
  //  \code{try/catch} block should any errors occur or exceptions be thrown.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  try
  {
    reader->Update();
    const InternalImageType * inputImage = reader->GetOutput();
    fastMarching->SetOutputRegion(inputImage->GetBufferedRegion());
    fastMarching->SetOutputSpacing(inputImage->GetSpacing());
    fastMarching->SetOutputOrigin(inputImage->GetOrigin());
    fastMarching->SetOutputDirection(inputImage->GetDirection());
    writer->Update();
  }
  catch (const itk::ExceptionObject & excep)
  {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
    return EXIT_FAILURE;
  }
  // Software Guide : EndCodeSnippet
 
  // Print out some useful information
  std::cout << std::endl;
  std::cout << "Max. no. iterations: "
            << thresholdSegmentation->GetNumberOfIterations() << std::endl;
  std::cout << "Max. RMS error: "
            << thresholdSegmentation->GetMaximumRMSError() << std::endl;
  std::cout << std::endl;
  std::cout << "No. elpased iterations: "
            << thresholdSegmentation->GetElapsedIterations() << std::endl;
  std::cout << "RMS change: " << thresholdSegmentation->GetRMSChange()
            << std::endl;
 
 
  // We write out some intermediate images for debugging.  These images can
  // help tune parameters.
  //
  using InternalWriterType = itk::ImageFileWriter<InternalImageType>;
 
  InternalWriterType::Pointer mapWriter = InternalWriterType::New();
  mapWriter->SetInput(fastMarching->GetOutput());
  mapWriter->SetFileName("fastMarchingImage.mha");
  mapWriter->Update();
 
  InternalWriterType::Pointer speedWriter = InternalWriterType::New();
  speedWriter->SetInput(thresholdSegmentation->GetSpeedImage());
  speedWriter->SetFileName("speedTermImage.mha");
  speedWriter->Update();
 
  //  Software Guide : BeginLatex
  //
  //  Let's run this application with the same data and parameters as the
  //  example given for \doxygen{ConnectedThresholdImageFilter} in
  //  Section~\ref{sec:ConnectedThreshold}. We will use a value of 5 as the
  //  initial distance of the surface from the seed points.  The algorithm is
  //  relatively insensitive to this initialization.  Compare the results in
  //  Figure~\ref{fig:ThresholdSegmentationLevelSetImageFilter} with those in
  //  Figure~\ref{fig:ConnectedThresholdOutput}. Notice how the smoothness
  //  constraint on the surface prevents leakage of the segmentation into
  //  both ventricles, but also localizes the segmentation to a smaller
  //  portion of the gray matter.
  //
  //  \begin{figure}
  //  \includegraphics[width=0.24\textwidth]{BrainProtonDensitySlice}
  //  \includegraphics[width=0.24\textwidth]{ThresholdSegmentationLevelSetImageFilterWhiteMatter}
  //  \includegraphics[width=0.24\textwidth]{ThresholdSegmentationLevelSetImageFilterVentricle}
  //  \includegraphics[width=0.24\textwidth]{ThresholdSegmentationLevelSetImageFilterGrayMatter}
  // \itkcaption[ThresholdSegmentationLevelSet segmentations]{Images
  // generated by the segmentation process based on the
  // ThresholdSegmentationLevelSetImageFilter. From left to right:
  // segmentation of the left ventricle, segmentation of the right ventricle,
  // segmentation of the white matter, attempt of segmentation of the gray
  // matter. The parameters used in this segmentations are presented in
  // Table~\ref{tab:ThresholdSegmentationLevelSetImageFilter}.}
  // \label{fig:ThresholdSegmentationLevelSetImageFilter} \end{figure}
  //
  //  \begin{table}
  //  \begin{center}
  //  \begin{tabular}{|l|c|c|c|c|c|}
  //  \hline
  //  Structure & Seed Index & Lower & Upper & Output Image \\ \hline
  //  White matter & $(60,116)$ & 150 & 180 & Second from left  \\  \hline
  //  Ventricle    & $(81,112)$ & 210 & 250 & Third  from left  \\  \hline
  //  Gray matter  & $(107,69)$ & 180 & 210 & Fourth from left  \\  \hline
  //  \end{tabular}
  //  \itkcaption[ThresholdSegmentationLevelSet segmentation parameters]
  //  {Segmentation results using the
  //  ThresholdSegmentationLevelSetImageFilter for various seed points. The
  //  resulting images are shown in
  //  Figure~\ref{fig:ThresholdSegmentationLevelSetImageFilter}
  //  \label{tab:ThresholdSegmentationLevelSetImageFilter}.}\end{center}
  //  \end{table}
  //
  //  Software Guide : EndLatex
 
  return EXIT_SUCCESS;
}