Examples/Filtering/GradientVectorFlowImageFilter.cxx

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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {GradientRecursiveGaussianImageFilterTest.mha}
//    ARGUMENTS: {GradientVectorFlowImageFilterOutput.mha}
//    ARGUMENTS:    5 2000.0
//  Software Guide : EndCommandLineArgs
 
//  Software Guide : BeginLatex
//
//  The \doxygen{GradientVectorFlowImageFilter} smooths multi-components
//  images such as vector fields and color images by applying a computation of
//  the diffusion equation.  A typical use of this filter is to smooth the
//  vector field resulting from computing the gradient of an image, with the
//  purpose of using the smoothed field in order to guide a deformable model.
//
//  The input image must be a multi-components images.
//
//  \index{itk::GradientVectorFlowImageFilter}
//
//  Software Guide : EndLatex
 
 
#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::GradientVectorFlowImageFilter!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkGradientVectorFlowImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 5)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputImageFile";
    std::cerr << " numberOfIterations  noiseLevel" << std::endl;
    return EXIT_FAILURE;
  }
 
  //  Software Guide : BeginLatex
  //
  //  Types should be selected based on the pixel types required for the input
  //  and output images. In this particular case, the input and output pixel
  //  types are multicomponents type such as itk::Vectors.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 3;
  using InputValueType = float;
  using OutputValueType = float;
  using InputPixelType = itk::Vector<InputValueType, Dimension>;
  using OutputPixelType = itk::Vector<OutputValueType, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  With them, the input and output image types can be instantiated.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InputImageType = itk::Image<InputPixelType, Dimension>;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  using ReaderType = itk::ImageFileReader<InputImageType>;
 
 
  //  Software Guide : BeginLatex
  //
  //  The GradientVectorFlow filter type is now instantiated using both the
  //  input image and the output image types.
  //
  //  \index{itk::GradientVectorFlowImageFilter!instantiation}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::GradientVectorFlowImageFilter<InputImageType, OutputImageType>;
  // Software Guide : EndCodeSnippet
 
 
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);
 
 
  //  Software Guide : BeginLatex
  //
  //  A filter object is created by invoking the \code{New()} method and
  //  assigning the result to a \doxygen{SmartPointer}.
  //
  //  \index{itk::GradientVectorFlowImageFilter!New()}
  //  \index{itk::GradientVectorFlowImageFilter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto filter = FilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  //  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       noiseLevel = std::stod(argv[4]);
 
 
  //  Software Guide : BeginLatex
  //
  //  The GradientVectorFlow filter requires two parameters, the number of
  //  iterations to be performed and the noise level of the input image. The
  //  noise level will be used to estimate the time step that should be used
  //  in the computation of the diffusion. These two parameters are set using
  //  the methods \code{SetNumberOfIterations()} and \code{SetNoiseLevel()}
  //  respectively.  Then the filter can be executed by invoking
  //  \code{Update()}.
  //
  //  \index{itk::GradientVectorFlowImageFilter!Update()}
  //  \index{itk::GradientVectorFlowImageFilter!SetNoiseLevel()}
  //  \index{itk::GradientVectorFlowImageFilter!SetNumberOfIterations()}
  //  \index{SetNoiseLevel()!itk::GradientVectorFlowImageFilter}
  //  \index{SetNumberOfIterations()!itk::GradientVectorFlowImageFilter}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetIterationNum(numberOfIterations);
  filter->SetNoiseLevel(noiseLevel);
  filter->Update();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  When using as input the result of a gradient filter, then the typical
  //  values for the noise level will be around 2000.0.
  //
  //  Software Guide : EndLatex
 
 
  //  Software Guide : BeginLatex
  //
  //  If the output of this filter has been connected to other filters down
  //  the pipeline, updating any of the downstream filters will
  //  triggered the execution of this one. For example, a writer filter could
  //  have been used after the curvature flow filter.
  //
  //  Software Guide : EndLatex
  using WriterType = itk::ImageFileWriter<OutputImageType>;
  auto writer = WriterType::New();
  writer->SetFileName(argv[2]);
 
  // Software Guide : BeginCodeSnippet
  writer->SetInput(filter->GetOutput());
  writer->Update();
  // Software Guide : EndCodeSnippet
 
 
  // In order to visualize the resulting vector field you could use ParaView
  // or VV (the 4D Slicer).
 
  return EXIT_SUCCESS;
}