ITK  5.2.0
Insight Toolkit
Examples/Filtering/GradientAnisotropicDiffusionImageFilter.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
*
* 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.
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*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {GradientAnisotropicDiffusionImageFilterOutput.png}
// ARGUMENTS: 15 0.1 3
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{GradientAnisotropicDiffusionImageFilter} implements an
// $N$-dimensional version of the classic Perona-Malik anisotropic diffusion
// equation for scalar-valued images \cite{Perona1990}.
//
// The conductance term for this implementation is chosen as a function of
// the gradient magnitude of the image at each point, reducing the strength
// of diffusion at edge pixels.
//
// \begin{equation}
// C(\mathbf{x}) = e^{-(\frac{\parallel \nabla U(\mathbf{x})
// \parallel}{K})^2} \end{equation}
//
// The numerical implementation of this equation is similar to that described
// in the Perona-Malik paper \cite{Perona1990}, but uses a more robust
// technique for gradient magnitude estimation and has been generalized to
// $N$-dimensions.
//
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// 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" << 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
// 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::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!instantiation}
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!New()}
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using FilterType =
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]);
// Software Guide : BeginLatex
//
// This filter requires three parameters: the number of iterations to be
// performed, the time step and the conductance parameter used in the
// computation of the level set evolution. These parameters are set using
// the methods \code{SetNumberOfIterations()}, \code{SetTimeStep()} and
// \code{SetConductanceParameter()} respectively. The filter can be
// executed by invoking \code{Update()}.
//
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!Update()}
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetTimeStep()}
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetConductanceParameter()}
// \index{itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter!SetNumberOfIterations()}
// \index{SetTimeStep()!itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
// \index{SetNumberOfIterations()!itk::Gradient\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNumberOfIterations(numberOfIterations);
filter->SetTimeStep(timeStep);
filter->SetConductanceParameter(conductance);
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Typical values for the time step are $0.25$ in $2D$ images and $0.125$
// in $3D$ images. The number of iterations is typically set to $5$; more
// iterations result in further smoothing and will increase the computing
// time linearly.
//
// Software Guide : EndLatex
//
// The output of the filter is rescaled here and then sent to a writer.
//
using WritePixelType = unsigned char;
using WriteImageType = itk::Image<WritePixelType, 2>;
using RescaleFilterType =
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
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]{GradientAnisotropicDiffusionImageFilterOutput}
// \itkcaption[GradientAnisotropicDiffusionImageFilter output]{Effect of the
// GradientAnisotropicDiffusionImageFilter on a slice from a MRI Proton
// Density image of the brain.}
// \label{fig:GradientAnisotropicDiffusionImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:GradientAnisotropicDiffusionImageFilterInputOutput}
// 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
// $0.25$, and $5$ iterations. The figure shows how homogeneous regions
// are
// smoothed and edges are preserved.
//
// \relatedClasses
// \begin{itemize}
// \item \doxygen{BilateralImageFilter}
// \item \doxygen{CurvatureAnisotropicDiffusionImageFilter}
// \item \doxygen{CurvatureFlowImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkGradientAnisotropicDiffusionImageFilter.h
itkImageFileReader.h
itkImage.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::GradientAnisotropicDiffusionImageFilter
This filter performs anisotropic diffusion on a scalar itk::Image using the classic Perona-Malik,...
Definition: itkGradientAnisotropicDiffusionImageFilter.h:51