Examples/Filtering/BinaryMinMaxCurvatureFlowImageFilter.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {BinaryMinMaxCurvatureFlowImageFilterOutput.png}
//    ARGUMENTS:    10 0.125 1 128
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginLatex
//
//  The \doxygen{BinaryMinMaxCurvatureFlowImageFilter} applies a variant of
//  the CurvatureFlow algorithm. Which means that the speed of propagation is
//  proportional to the curvature $\kappa$ of iso-contours. This filter adds
//  however, the restriction that negative curvatures are only accepted in
//  regions of the image having low intensities. The user should provide an
//  intensity threshold over which negative curvatures are not considered for
//  the propagation.
//
//  In practice the algorithm do the following for each pixel. First, the
//  curvature $\kappa$ is computed on the current pixel. If the computed
//  curvature is null this is returned as value.  Otherwise, an average of
//  neighbor pixel intensities is computed and it is compared against a
//  user-provided threshold. If this average is less than the threshold then
//  the algorithm returns $\min(\kappa,0)$. If the average intensity is
//  greater or equal than user-provided threshold, then the returned value is
//  $\max(\kappa,0)$.
//
//  \begin{equation}
//  I_t = F |\nabla I|
//  \end{equation}
//
//  where $F$ is defined as
//
//  \begin{equation}
//  F = \left\{ \begin{array} {r@{\quad:\quad}l} \min(\kappa,0) &
//  \mbox{Average} < \mbox{Threshold} \\ \max(\kappa,0) & \mbox{Average} \ge
//  \mbox{Threshold} \end{array} \right.
//  \end{equation}
//
//  \index{itk::Binary\-MinMax\-Curvature\-Flow\-Image\-Filter}
//
//  Software Guide : EndLatex
 
 
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
 
//  Software Guide : BeginLatex
//
//  The first step required for using this filter is to include its header
//  file
//
//  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkBinaryMinMaxCurvatureFlowImageFilter.h"
// Software Guide : EndCodeSnippet
 
int
main(int argc, char * argv[])
{
 
 
  if (argc < 7)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputImageFile  ";
    std::cerr << "numberOfIterations  timeStep  stencilRadius  threshold"
              << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Types should be chosen for the pixels of the input and output images and
  //  with them the image types are instantiated.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InputPixelType = float;
  using OutputPixelType = float;
 
  using InputImageType = itk::Image<InputPixelType, 2>;
  using OutputImageType = itk::Image<OutputPixelType, 2>;
  // Software Guide : EndCodeSnippet
 
  using ReaderType = itk::ImageFileReader<InputImageType>;
 
 
  //  Software Guide : BeginLatex
  //
  //  The BinaryMinMaxCurvatureFlowFilter type is now instantiated using both
  //  the input image and the output image types. The filter is then created
  //  using the \code{New()} method.
  //
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!instantiation}
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!New()}
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::BinaryMinMaxCurvatureFlowImageFilter<InputImageType,
                                              OutputImageType>;
 
  auto filter = FilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);
 
 
  //  Software Guide : BeginLatex
  //
  //  The input image can be obtained from the output of another filter. Here,
  //  an image reader is used as source.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetInput(reader->GetOutput());
  // Software Guide : EndCodeSnippet
 
 
  const unsigned int numberOfIterations = std::stoi(argv[3]);
 
  const double timeStep = std::stod(argv[4]);
 
  using RadiusType = FilterType::RadiusValueType;
 
  const RadiusType radius = atol(argv[5]);
 
  const double threshold = std::stod(argv[6]);
 
 
  //  Software Guide : BeginLatex
  //
  //  The \doxygen{BinaryMinMaxCurvatureFlowImageFilter} requires the same
  //  parameters of the MinMaxCurvatureFlowImageFilter plus the value of the
  //  threshold against which the neighborhood average will be compared. The
  //  threshold is passed using the \code{SetThreshold()} method. Then the
  //  filter can be executed by invoking \code{Update()}.
  //
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!Update()}
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetTimeStep()}
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetNumberOfIterations()}
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetStencilRadius()}
  //  \index{itk::BinaryMinMaxCurvatureFlowImageFilter!SetThreshold()}
  //  \index{SetTimeStep()!itk::BinaryMinMaxCurvatureFlowImageFilter}
  //  \index{SetStencilRadius()!itk::BinaryMinMaxCurvatureFlowImageFilter}
  //  \index{SetThreshold()!itk::BinaryMinMaxCurvatureFlowImageFilter}
  //  \index{SetNumberOfIterations()!itk::BinaryMinMaxCurvatureFlowImageFilter}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetTimeStep(timeStep);
  filter->SetNumberOfIterations(numberOfIterations);
 
  filter->SetStencilRadius(radius);
  filter->SetThreshold(threshold);
 
  filter->Update();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Typical values for the time step are $0.125$ in $2D$ images and $0.0625$
  //  in $3D$ images. The number of iterations can be usually around $10$,
  //  more iterations will result in further smoothing and will increase
  //  linearly the computing time. The radius of the stencil can be typically
  //  $1$. The value of the threshold should be selected according to the gray
  //  levels of the object of interest and the gray level of its background.
  //
  //  Software Guide : EndLatex
 
  using WritePixelType = unsigned char;
 
  using WriteImageType = itk::Image<WritePixelType, 2>;
 
  using RescaleFilterType =
    itk::RescaleIntensityImageFilter<OutputImageType, WriteImageType>;
 
  auto rescaler = RescaleFilterType::New();
 
  rescaler->SetOutputMinimum(0);
  rescaler->SetOutputMaximum(255);
 
 
  using WriterType = itk::ImageFileWriter<WriteImageType>;
 
  auto writer = WriterType::New();
 
  writer->SetFileName(argv[2]);
 
 
  rescaler->SetInput(filter->GetOutput());
  writer->SetInput(rescaler->GetOutput());
  writer->Update();
 
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
  // \includegraphics[width=0.44\textwidth]{BinaryMinMaxCurvatureFlowImageFilterOutput}
  // \itkcaption[BinaryMinMaxCurvatureFlowImageFilter output]{Effect of the
  // BinaryMinMaxCurvatureFlowImageFilter on a slice from a MRI proton density
  // image  of the brain.}
  // \label{fig:BinaryMinMaxCurvatureFlowImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:BinaryMinMaxCurvatureFlowImageFilterInputOutput}
  //  illustrates the effect of this filter on a MRI proton density image of
  //  the brain. In this example the filter was run with a time step of
  //  $0.125$, $10$ iterations, a stencil radius of $1$ and a threshold of
  //  $128$.
  //
  //  Software Guide : EndLatex
 
 
  return EXIT_SUCCESS;
}