Examples/Filtering/CurvatureAnisotropicDiffusionImageFilter.cxx

/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://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>;
 
  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]);
  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>;
 
  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]{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;
}