Examples/Filtering/VectorCurvatureAnisotropicDiffusionImageFilter.cxx

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//  Software Guide : BeginLatex
//
//  The \doxygen{VectorCurvatureAnisotropicDiffusionImageFilter} performs
//  anisotropic diffusion on a vector image using a modified curvature
//  diffusion equation (MCDE).  The MCDE is the same described in
//  \ref{sec:CurvatureAnisotropicDiffusionImageFilter}.
//
//  Typically in vector-valued diffusion, vector components are diffused
//  independently of one another using a conductance term that is linked
//  across the components.
//
//  This filter is designed to process images of \doxygen{Vector} type.  The
//  code relies on various type alias and overloaded operators defined in
//  \doxygen{Vector}. It is perfectly reasonable, however, to apply this
//  filter to images of other, user-defined types as long as the appropriate
//  type alias and operator overloads are in place.  As a general rule, follow
//  the example of the \doxygen{Vector} class in defining your data types.
//
//  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
//
//  Software Guide : EndLatex
 
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkGradientRecursiveGaussianImageFilter.h"
#include "itkVectorIndexSelectionCastImageFilter.h"
 
 
//  Software Guide : BeginLatex
//
//  The first step required to use this filter is to include its header file.
//
//  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkVectorCurvatureAnisotropicDiffusionImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 6)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputGradientImageFile ";
    std::cerr << "outputSmoothedGradientImageFile ";
    std::cerr << "numberOfIterations  timeStep  " << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Types should be selected based on required pixel type 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 VectorPixelType = itk::CovariantVector<float, 2>;
  using InputImageType = itk::Image<InputPixelType, 2>;
  using VectorImageType = itk::Image<VectorPixelType, 2>;
  // Software Guide : EndCodeSnippet
 
 
  using ReaderType = itk::ImageFileReader<InputImageType>;
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);
 
 
  //  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::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!instantiation}
  //  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!New()}
  //  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::VectorCurvatureAnisotropicDiffusionImageFilter<VectorImageType,
                                                        VectorImageType>;
  auto filter = FilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  using GradientFilterType =
    itk::GradientRecursiveGaussianImageFilter<InputImageType,
                                              VectorImageType>;
 
  auto gradient = GradientFilterType::New();
 
 
  //  Software Guide : BeginLatex
  //
  //  The input image can be obtained from the output of another filter. Here,
  //  an image reader is used as source and its data is passed through a
  //  gradient filter in order to generate an image of vectors.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  gradient->SetInput(reader->GetOutput());
  filter->SetInput(gradient->GetOutput());
  // Software Guide : EndCodeSnippet
 
  const unsigned int numberOfIterations = std::stoi(argv[4]);
  const double       timeStep = std::stod(argv[5]);
 
 
  //  Software Guide : BeginLatex
  //
  //  This filter requires two parameters: the number of iterations to be
  //  performed and the time step used in the computation of the level set
  //  evolution. These parameters are set using the methods
  //  \code{SetNumberOfIterations()} and \code{SetTimeStep()} respectively.
  //  The filter can be executed by invoking \code{Update()}.
  //
  //  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!Update()}
  //  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetTimeStep()}
  //  \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetNumberOfIterations()}
  //  \index{SetTimeStep()!itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
  //  \index{SetNumberOfIterations()!itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetNumberOfIterations(numberOfIterations);
  filter->SetTimeStep(timeStep);
  filter->SetConductanceParameter(1.0);
  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
  //  $5$, however more iterations will result in further smoothing and will
  //  increase the computing time linearly.
  //
  //  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 OutputPixelType = float;
  using OutputImageType = itk::Image<OutputPixelType, 2>;
  using ComponentFilterType =
    itk::VectorIndexSelectionCastImageFilter<VectorImageType,
                                             OutputImageType>;
  auto component = ComponentFilterType::New();
 
  // Select the component to extract.
  component->SetIndex(0);
 
  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();
  rescaler->SetInput(component->GetOutput());
  writer->SetInput(rescaler->GetOutput());
 
  // Save the component of the original gradient
  component->SetInput(gradient->GetOutput());
  writer->SetFileName(argv[2]);
  writer->Update();
 
 
  // Save the component of the smoothed gradient
  component->SetInput(filter->GetOutput());
  writer->SetFileName(argv[3]);
  writer->Update();
 
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure} \center
  // \includegraphics[width=0.44\textwidth]{VectorCurvatureAnisotropicDiffusionImageFilterInput}
  // \includegraphics[width=0.44\textwidth]{VectorCurvatureAnisotropicDiffusionImageFilterOutput}
  // \itkcaption[VectorCurvatureAnisotropicDiffusionImageFilter output]{Effect
  // of the VectorCurvatureAnisotropicDiffusionImageFilter on the $X$
  // component of the gradient from a MRI proton density brain image.}
  // \label{fig:VectorCurvatureAnisotropicDiffusionImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure~\ref{fig:VectorCurvatureAnisotropicDiffusionImageFilterInputOutput}
  //  illustrates the effect of this filter on a MRI proton density image of
  //  the brain. The images show the $X$ component of the gradient before
  //  (left) and after (right) the application of the filter. In this example
  //  the filter was run with a time step of 0.25, and 5 iterations.
  //
  //  Software Guide : EndLatex
 
  return EXIT_SUCCESS;
}