ITK  5.4.0 Insight Toolkit
Examples/Filtering/CurvatureAnisotropicDiffusionImageFilter.cxx
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// 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:
//
//
// f_t = \mid \nabla f \mid \nabla \cdot c( \mid \nabla f \mid ) \frac{
// \nabla f }{ \mid \nabla f \mid }
//
//
// where the conductance modified curvature term is
//
//
// \nabla \cdot \frac{\nabla f}{\mid \nabla f \mid}
//
//
// \index{itk::Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required for using this filter is to include its header
// file.
//
//
// 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 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
// 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 =
OutputImageType>;
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
// 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 =
auto rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum(0);
rescaler->SetOutputMaximum(255);
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}
// \end{itemize}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itkImage.h
Data source that reads image data from a single file.
itk::CurvatureAnisotropicDiffusionImageFilter
This filter performs anisotropic diffusion on a scalar itk::Image using the modified curvature diffus...
itkCurvatureAnisotropicDiffusionImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:88
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:147
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
New
static Pointer New()