Examples/Segmentation/WatershedSegmentation1.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {WatershedSegmentation1Output1.png}
//    ARGUMENTS:    2 10 0 0.05 1
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {WatershedSegmentation1Output2.png}
//    ARGUMENTS:    2 10 0.001 0.15 0
//  Software Guide : EndCommandLineArgs
 
// Software Guide : BeginLatex
//
// The following example illustrates how to preprocess and segment images
// using the \doxygen{WatershedImageFilter}. Note that the care with which
// the data are preprocessed will greatly affect the quality of your result.
// Typically, the best results are obtained by preprocessing the original
// image with an edge-preserving diffusion filter, such as one of the
// anisotropic diffusion filters, or the bilateral image filter.  As
// noted in Section~\ref{sec:AboutWatersheds}, the height function used as
// input should be created such that higher positive values correspond to
// object boundaries.  A suitable height function for many applications can
// be generated as the gradient magnitude of the image to be segmented.
//
// The \doxygen{VectorGradientMagnitudeAnisotropicDiffusionImageFilter} class
// is used to smooth the image and the
// \doxygen{VectorGradientMagnitudeImageFilter} is used to generate the
// height function.  We begin by including all preprocessing filter header
// files and the header file for the WatershedImageFilter.  We
// use the vector versions of these filters because the input dataset is a
// color image.
//
//
// Software Guide : EndLatex
#include <iostream>
 
// Software Guide : BeginCodeSnippet
#include "itkVectorGradientAnisotropicDiffusionImageFilter.h"
#include "itkVectorGradientMagnitudeImageFilter.h"
#include "itkWatershedImageFilter.h"
// Software Guide : EndCodeSnippet
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkCastImageFilter.h"
#include "itkScalarToRGBPixelFunctor.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 conductanceTerm diffusionIterations "
         "lowerThreshold outputScaleLevel gradientMode "
      << std::endl;
    return EXIT_FAILURE;
  }
 
  // Software Guide : BeginLatex
  //
  // We now declare the image and pixel types to use for instantiation of the
  // filters.  All of these filters expect real-valued pixel types in order to
  // work properly.  The preprocessing stages are applied directly to the
  // vector-valued data and the segmentation uses floating point
  // scalar data.  Images are converted from RGB pixel type to
  // numerical vector type using \doxygen{CastImageFilter}.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using RGBPixelType = itk::RGBPixel<unsigned char>;
  using RGBImageType = itk::Image<RGBPixelType, 2>;
  using VectorPixelType = itk::Vector<float, 3>;
  using VectorImageType = itk::Image<VectorPixelType, 2>;
  using LabeledImageType = itk::Image<itk::IdentifierType, 2>;
  using ScalarImageType = itk::Image<float, 2>;
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // The various image processing filters are declared using the types created
  // above and eventually used in the pipeline.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FileReaderType = itk::ImageFileReader<RGBImageType>;
  using CastFilterType = itk::CastImageFilter<RGBImageType, VectorImageType>;
  using DiffusionFilterType =
    itk::VectorGradientAnisotropicDiffusionImageFilter<VectorImageType,
                                                       VectorImageType>;
  using GradientMagnitudeFilterType =
    itk::VectorGradientMagnitudeImageFilter<VectorImageType>;
  using WatershedFilterType = itk::WatershedImageFilter<ScalarImageType>;
  // Software Guide : EndCodeSnippet
 
  using FileWriterType = itk::ImageFileWriter<RGBImageType>;
 
  auto reader = FileReaderType::New();
  reader->SetFileName(argv[1]);
 
  auto caster = CastFilterType::New();
 
  // Software Guide : BeginLatex
  //
  // Next we instantiate the filters and set their parameters.  The first
  // step in the image processing pipeline is diffusion of the color input
  // image using an anisotropic diffusion filter.  For this class of filters,
  // the CFL condition requires that the time step be no more than 0.25 for
  // two-dimensional images, and no more than 0.125 for three-dimensional
  // images.  The number of iterations and the conductance term will be taken
  // from the command line. See
  // Section~\ref{sec:EdgePreservingSmoothingFilters} for more information on
  // the ITK anisotropic diffusion filters.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto diffusion = DiffusionFilterType::New();
  diffusion->SetNumberOfIterations(std::stoi(argv[4]));
  diffusion->SetConductanceParameter(std::stod(argv[3]));
  diffusion->SetTimeStep(0.125);
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // The ITK gradient magnitude filter for vector-valued images can optionally
  // take several parameters.  Here we allow only enabling or disabling
  // of principal component analysis.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto gradient = GradientMagnitudeFilterType::New();
  gradient->SetUsePrincipleComponents(std::stoi(argv[7]));
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // Finally we set up the watershed filter.  There are two parameters.
  // \code{Level} controls watershed depth, and \code{Threshold} controls the
  // lower thresholding of the input.  Both parameters are set as a
  // percentage (0.0 - 1.0) of the maximum depth in the input image.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto watershed = WatershedFilterType::New();
  watershed->SetLevel(std::stod(argv[6]));
  watershed->SetThreshold(std::stod(argv[5]));
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // The output of WatershedImageFilter is an image of unsigned long integer
  // labels, where a label denotes membership of a pixel in a particular
  // segmented region.  This format is not practical for visualization, so
  // for the purposes of this example, we will convert it to RGB pixels.  RGB
  // images have the advantage that they can be saved as a simple png file
  // and viewed using any standard image viewer software.  The
  // \subdoxygen{Functor}{ScalarToRGBPixelFunctor} class is a special
  // function object designed to hash a scalar value into an
  // \doxygen{RGBPixel}. Plugging this functor into the
  // \doxygen{UnaryFunctorImageFilter} creates an image filter which
  // converts scalar images to RGB images.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using ColormapFunctorType =
    itk::Functor::ScalarToRGBPixelFunctor<unsigned long>;
  using ColormapFilterType =
    itk::UnaryFunctorImageFilter<LabeledImageType,
                                 RGBImageType,
                                 ColormapFunctorType>;
  auto colormapper = ColormapFilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  auto writer = FileWriterType::New();
  writer->SetFileName(argv[2]);
 
  // Software Guide : BeginLatex
  //
  // The filters are connected into a single pipeline, with readers and
  // writers at each end.
  //
  // Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  caster->SetInput(reader->GetOutput());
  diffusion->SetInput(caster->GetOutput());
  gradient->SetInput(diffusion->GetOutput());
  watershed->SetInput(gradient->GetOutput());
  colormapper->SetInput(watershed->GetOutput());
  writer->SetInput(colormapper->GetOutput());
  // Software Guide : EndCodeSnippet
 
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & e)
  {
    std::cerr << e << std::endl;
    return EXIT_FAILURE;
  }
 
  return EXIT_SUCCESS;
}
 
//
// Software Guide : BeginLatex
//
// \begin{figure} \center
// \includegraphics[width=0.32\textwidth]{VisibleWomanEyeSlice}
// \includegraphics[width=0.32\textwidth]{WatershedSegmentation1Output1}
// \includegraphics[width=0.32\textwidth]{WatershedSegmentation1Output2}
// \itkcaption[Watershed segmentation output]{Segmented section of Visible
// Human female head and neck cryosection data.  At left is the original
// image.  The image in the middle was generated with parameters: conductance
// = 2.0, iterations = 10, threshold = 0.0, level = 0.05, principal components
// = on. The image on the right was generated with parameters: conductance
// = 2.0, iterations = 10, threshold = 0.001, level = 0.15, principal
// components = off. } \label{fig:outputWatersheds} \end{figure}
//
//
// Tuning the filter parameters for any particular application is a process
// of trial and error.  The \emph{threshold} parameter can be used to great
// effect in controlling oversegmentation of the image.  Raising the
// threshold will generally reduce computation time and produce output with
// fewer and larger regions.  The trick in tuning parameters is to consider
// the scale level of the objects that you are trying to segment in the
// image.  The best time/quality trade-off will be achieved when the image is
// smoothed and thresholded to eliminate features just below the desired
// scale.
//
// Figure~\ref{fig:outputWatersheds} shows output from the example code.  The
// input image is taken from the Visible Human female data around the right
// eye.  The images on the right are colorized watershed segmentations with
// parameters set to capture objects such as the optic nerve and
// lateral rectus muscles, which can be seen just above and to the left and
// right of the eyeball.  Note that a critical difference between the two
// segmentations is the mode of the gradient magnitude calculation.
//
// A note on the computational complexity of the watershed algorithm is
// warranted.  Most of the complexity of the ITK implementation lies in
// generating the hierarchy. Processing times for this stage are non-linear
// with respect to the number of catchment basins in the initial segmentation.
// This means that the amount of information contained in an image is more
// significant than the number of pixels in the image.  A very large, but very
// flat input take less time to segment than a very small, but very detailed
// input.
//
// Software Guide : EndLatex