text: /*=========================================================================
   *
   *  Copyright Insight Software Consortium
   *
   *  Licensed under the Apache License, Version 2.0 (the "License");
   *  you may not use this file except in compliance with the License.
   *  You may obtain a copy of the License at
   *
   *         http://www.apache.org/licenses/LICENSE-2.0.txt
   *
   *  Unless required by applicable law or agreed to in writing, software
   *  distributed under the License is distributed on an "AS IS" BASIS,
   *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   *  See the License for the specific language governing permissions and
   *  limitations under the License.
   *
   *=========================================================================*/
  
  //  Software Guide : BeginCommandLineArgs
  //    INPUTS:  {BrainProtonDensitySlice.png}
  //    OUTPUTS: {CurvatureAnisotropicDiffusionImageFilterOutput.png}
  //    ARGUMENTS:    5 0.125 3
  //  Software Guide : EndCommandLineArgs
  //
  //  Software Guide : BeginLatex
  //
  //  The \doxygen{CurvatureAnisotropicDiffusionImageFilter} performs anisotropic
  //  diffusion on an image using a modified curvature diffusion equation (MCDE).
  //
  //  MCDE does not exhibit the edge enhancing properties of classic anisotropic
  //  diffusion, which can under certain conditions undergo a ``negative''
  //  diffusion, which enhances the contrast of edges.  Equations of the form of
  //  MCDE always undergo positive diffusion, with the conductance term only
  //  varying the strength of that diffusion.
  //
  //  Qualitatively, MCDE compares well with other non-linear diffusion
  //  techniques.  It is less sensitive to contrast than classic Perona-Malik
  //  style diffusion, and preserves finer detailed structures in images.
  //  There is a potential speed trade-off for using this function in place of
  //  itkGradientNDAnisotropicDiffusionFunction.  Each iteration of the
  //  solution takes roughly twice as long.  Fewer iterations, however, may be
  //  required to reach an acceptable solution.
  //
  //  The MCDE equation is given as:
  //
  //  \begin{equation}
  //  f_t = \mid \nabla f \mid \nabla \cdot c( \mid \nabla f \mid ) \frac{
  //  \nabla f }{ \mid \nabla f \mid }
  //  \end{equation}
  //
  //  where the conductance modified curvature term is
  //
  //  \begin{equation}
  //  \nabla \cdot \frac{\nabla f}{\mid \nabla f \mid}
  //  \end{equation}
  //
  //  \index{itk::Curvature\-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 for using this filter is to include its header file.
  //
  //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!header}
  //
  //  Software Guide : EndLatex
  
  // Software Guide : BeginCodeSnippet
  #include "itkCurvatureAnisotropicDiffusionImageFilter.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 useImageSpacingon/off"
                << 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::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!instantiation}
    //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!New()}
    //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!Pointer}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    using FilterType =
      itk::CurvatureAnisotropicDiffusionImageFilter<InputImageType, OutputImageType>;
  
    FilterType::Pointer filter = FilterType::New();
    // Software Guide : EndCodeSnippet
  
  
    ReaderType::Pointer 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]);
    const bool         useImageSpacing = (argc != 6);
  
    //  Software Guide : BeginLatex
    //
    //  This filter requires three parameters: the number of iterations to be
    //  performed, the time step used in the computation of the level set
    //  evolution and the value of conductance. These parameters are set using
    //  the methods \code{SetNumberOfIterations()}, \code{SetTimeStep()} and
    //  \code{SetConductance()} respectively.  The filter can be executed by
    //  invoking \code{Update()}.
    //
    //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!Update()}
    //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetTimeStep()}
    //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetNumberOfIterations()}
    //  \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetConductanceParameter()}
    //  \index{SetTimeStep()!itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
    //  \index{SetNumberOfIterations()!itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
    //  \index{SetConductanceParameter()!itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    filter->SetNumberOfIterations(numberOfIterations);
    filter->SetTimeStep(timeStep);
    filter->SetConductanceParameter(conductance);
    if (useImageSpacing)
    {
      filter->UseImageSpacingOn();
    }
    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 $5$, more
    //  iterations will result in further smoothing and will increase the
    //  computing time linearly. The conductance parameter is usually around $3.0$.
    //
    //  Software Guide : EndLatex
  
  
    //
    //  If the output of this filter has been connected to other filters down the
    //  pipeline, updating any of the downstream filters would have triggered the
    //  execution of this one. For example, a writer filter could have been used
    //  after the curvature flow filter.
    //
  
    using WritePixelType = unsigned char;
    using WriteImageType = itk::Image<WritePixelType, 2>;
    using RescaleFilterType =
      itk::RescaleIntensityImageFilter<OutputImageType, WriteImageType>;
  
    RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
    rescaler->SetOutputMinimum(0);
    rescaler->SetOutputMaximum(255);
  
    using WriterType = itk::ImageFileWriter<WriteImageType>;
  
    WriterType::Pointer 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]{CurvatureAnisotropicDiffusionImageFilterOutput}
    // \itkcaption[CurvatureAnisotropicDiffusionImageFilter output]{Effect of the
    // CurvatureAnisotropicDiffusionImageFilter on a slice from a MRI Proton
    // Density image  of the brain.}
    // \label{fig:CurvatureAnisotropicDiffusionImageFilterInputOutput}
    // \end{figure}
    //
    //  Figure \ref{fig:CurvatureAnisotropicDiffusionImageFilterInputOutput}
    //  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
    //  $0.125$, $5$ iterations and a conductance value of $3.0$.  The figure
    //  shows how homogeneous regions are smoothed and edges are preserved.
    //
    //  \relatedClasses
    //  \begin{itemize}
    //  \item \doxygen{BilateralImageFilter}
    //  \item \doxygen{CurvatureFlowImageFilter}
    //  \item \doxygen{GradientAnisotropicDiffusionImageFilter}
    //  \end{itemize}
    //
    //  Software Guide : EndLatex
  
    return EXIT_SUCCESS;
  }
  