ITK  4.13.0
Insight Segmentation and Registration Toolkit
Examples/Filtering/CurvatureAnisotropicDiffusionImageFilter.cxx
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*
* Copyright Insight Software Consortium
*
* 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.
*
*=========================================================================*/
// 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:
//
// \begin{equation}
// f_t = \mid \nabla f \mid \nabla \cdot c( \mid \nabla f \mid ) \frac{
// \nabla f }{ \mid \nabla f \mid }
// \end{equation}
//
// where the conductance modified curvature term is
//
// \begin{equation}
// \nabla \cdot \frac{\nabla f}{\mid \nabla f \mid}
// \end{equation}
//
// \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.
//
// \index{itk::Curvature\-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 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
typedef float InputPixelType;
typedef float OutputPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// 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
InputImageType, OutputImageType > FilterType;
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 = atoi( argv[3] );
const double timeStep = atof( argv[4] );
const double conductance = atof( 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.
//
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
OutputImageType, WriteImageType > 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]{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}
// \item \doxygen{GradientAnisotropicDiffusionImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}