Compute Gradient Anisotropic Diffusion#

Synopsis#

Perform anisotropic diffusion on an image.

Results#

Code#

Python#

#!/usr/bin/env python

import itk
import argparse

parser = argparse.ArgumentParser(description="Compute Gradient Anisotropic Diffusion.")
parser.add_argument("input_image")
parser.add_argument("output_image")
parser.add_argument("number_of_iterations", type=int)
parser.add_argument("time_step", type=float)
parser.add_argument("conductance", type=float)
args = parser.parse_args()

InputPixelType = itk.F
OutputPixelType = itk.UC
Dimension = 2

InputImageType = itk.Image[InputPixelType, Dimension]
OutputImageType = itk.Image[OutputPixelType, Dimension]

ReaderType = itk.ImageFileReader[InputImageType]
reader = ReaderType.New()
reader.SetFileName(args.input_image)

FilterType = itk.GradientAnisotropicDiffusionImageFilter[InputImageType, InputImageType]
gradientAnisotropicDiffusionFilter = FilterType.New()

gradientAnisotropicDiffusionFilter.SetInput(reader.GetOutput())
gradientAnisotropicDiffusionFilter.SetNumberOfIterations(args.number_of_iterations)
gradientAnisotropicDiffusionFilter.SetTimeStep(args.time_step)
gradientAnisotropicDiffusionFilter.SetConductanceParameter(args.conductance)

RescaleFilterType = itk.RescaleIntensityImageFilter[InputImageType, OutputImageType]
rescaler = RescaleFilterType.New()
rescaler.SetInput(gradientAnisotropicDiffusionFilter.GetOutput())

outputPixelTypeMinimum = itk.NumericTraits[OutputPixelType].min()
outputPixelTypeMaximum = itk.NumericTraits[OutputPixelType].max()

rescaler.SetOutputMinimum(outputPixelTypeMinimum)
rescaler.SetOutputMaximum(outputPixelTypeMaximum)

WriterType = itk.ImageFileWriter[OutputImageType]
writer = WriterType.New()
writer.SetFileName(args.output_image)
writer.SetInput(rescaler.GetOutput())

writer.Update()

C++#

#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkGradientAnisotropicDiffusionImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"

int
main(int argc, char * argv[])
{
  if (argc != 6)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0];
    std::cerr << " <InputFileName> <OutputFileName>";
    std::cerr << " <numberOfIterations> <timeStep> <conductance>";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }

  const char * inputFileName = argv[1];
  const char * outputFileName = argv[2];

  constexpr unsigned int Dimension = 2;

  using InputPixelType = float;
  using InputImageType = itk::Image<InputPixelType, Dimension>;
  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;

  const int            numberOfIterations = std::stoi(argv[3]);
  const InputPixelType timeStep = std::stod(argv[4]);
  const InputPixelType conductance = std::stod(argv[5]);

  const auto input = itk::ReadImage<InputImageType>(inputFileName);

  using FilterType = itk::GradientAnisotropicDiffusionImageFilter<InputImageType, InputImageType>;
  auto filter = FilterType::New();
  filter->SetInput(input);
  filter->SetNumberOfIterations(numberOfIterations);
  filter->SetTimeStep(timeStep);
  filter->SetConductanceParameter(conductance);

  using RescaleType = itk::RescaleIntensityImageFilter<InputImageType, OutputImageType>;
  auto rescaler = RescaleType::New();
  rescaler->SetInput(filter->GetOutput());
  rescaler->SetOutputMinimum(itk::NumericTraits<OutputPixelType>::min());
  rescaler->SetOutputMaximum(itk::NumericTraits<OutputPixelType>::max());

  try
  {
    itk::WriteImage(rescaler->GetOutput(), outputFileName);
  }
  catch (const itk::ExceptionObject & error)
  {
    std::cerr << "Error: " << error << std::endl;
    return EXIT_FAILURE;
  }

  return EXIT_SUCCESS;
}

Classes demonstrated#

template<typename TInputImage, typename TOutputImage> class GradientAnisotropicDiffusionImageFilter : public itk::AnisotropicDiffusionImageFilter<TInputImage, TOutputImage>

This filter performs anisotropic diffusion on a scalar itk::Image using the classic Perona-Malik, gradient magnitude based equation.

For detailed information on anisotropic diffusion, see itkAnisotropicDiffusionFunction and itkGradientNDAnisotropicDiffusionFunction.

Inputs and Outputs: The input to this filter should be a scalar itk::Image of any dimensionality. The output image will be a diffused copy of the input.
Parameters: Please see the description of parameters given in itkAnisotropicDiffusionImageFilter.
See: AnisotropicDiffusionImageFilter
See: AnisotropicDiffusionFunction
See: GradientAnisotropicDiffusionFunction

See itk::GradientAnisotropicDiffusionImageFilter for additional documentation.