Examples/Filtering/GradientAnisotropicDiffusionImageFilter.cxx

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 *  Copyright NumFOCUS
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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {GradientAnisotropicDiffusionImageFilterOutput.png}
//    ARGUMENTS:    15 0.1 3
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginLatex
//
//  The \doxygen{GradientAnisotropicDiffusionImageFilter}  implements an
//  $N$-dimensional version of the classic Perona-Malik anisotropic diffusion
//  equation for scalar-valued images \cite{Perona1990}.
//
//  The conductance term for this implementation is chosen as a function of
//  the gradient magnitude of the image at each point, reducing the strength
//  of diffusion at edge pixels.
//
//  \begin{equation}
//  C(\mathbf{x}) = e^{-(\frac{\parallel \nabla U(\mathbf{x})
//  \parallel}{K})^2} \end{equation}
//
//  The numerical implementation of this equation is similar to that described
//  in the Perona-Malik paper \cite{Perona1990}, but uses a more robust
//  technique for gradient magnitude estimation and has been generalized to
//  $N$-dimensions.
//
//  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
//
//  Software Guide : EndLatex
 
 
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
 
//  Software Guide : BeginLatex
//
//  The first step required to use this filter is to include its header file.
//
//  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkGradientAnisotropicDiffusionImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 6)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputImageFile ";
    std::cerr << "numberOfIterations  timeStep  conductance" << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Types should be selected based on the pixel types required for the
  //  input and output images.  The image types are defined using the pixel
  //  type and the dimension.
  //
  //  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 filter type is now instantiated using both the input image and the
  //  output image types. The filter object is created by the \code{New()}
  //  method.
  //
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!instantiation}
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!New()}
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::GradientAnisotropicDiffusionImageFilter<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]);
 
  const double conductance = std::stod(argv[5]);
 
 
  //  Software Guide : BeginLatex
  //
  //  This filter requires three parameters: the number of iterations to be
  //  performed, the time step and the conductance parameter used in the
  //  computation of the level set evolution. These parameters are set using
  //  the methods \code{SetNumberOfIterations()}, \code{SetTimeStep()} and
  //  \code{SetConductanceParameter()} respectively.  The filter can be
  //  executed by invoking \code{Update()}.
  //
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!Update()}
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetTimeStep()}
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetConductanceParameter()}
  //  \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetNumberOfIterations()}
  //  \index{SetTimeStep()!itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
  //  \index{SetNumberOfIterations()!itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetNumberOfIterations(numberOfIterations);
  filter->SetTimeStep(timeStep);
  filter->SetConductanceParameter(conductance);
 
  filter->Update();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Typical values for the time step are $0.25$ in $2D$ images and $0.125$
  //  in $3D$ images. The number of iterations is typically set to $5$; more
  //  iterations result in further smoothing and will increase the computing
  //  time linearly.
  //
  //  Software Guide : EndLatex
 
 
  //
  //  The output of the filter is rescaled here and then sent to a writer.
  //
  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]{GradientAnisotropicDiffusionImageFilterOutput}
  // \itkcaption[GradientAnisotropicDiffusionImageFilter output]{Effect of the
  // GradientAnisotropicDiffusionImageFilter on a slice from a MRI Proton
  // Density image  of the brain.}
  // \label{fig:GradientAnisotropicDiffusionImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:GradientAnisotropicDiffusionImageFilterInputOutput}
  //  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
  //  $0.25$, and $5$ iterations.  The figure shows how homogeneous regions
  //  are
  // smoothed and edges are preserved.
  //
  //  \relatedClasses
  //  \begin{itemize}
  //  \item \doxygen{BilateralImageFilter}
  //  \item \doxygen{CurvatureAnisotropicDiffusionImageFilter}
  //  \item \doxygen{CurvatureFlowImageFilter}
  //  \end{itemize}
  //
  //  Software Guide : EndLatex
 
 
  return EXIT_SUCCESS;
}