ITK  5.1.0
Insight Toolkit
Examples/Filtering/CurvatureFlowImageFilter.cxx
/*=========================================================================
*
* 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.
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*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {CurvatureFlowImageFilterOutput.png}
// ARGUMENTS: 10 0.25
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{CurvatureFlowImageFilter} performs edge-preserving smoothing
// in a similar fashion to the classical anisotropic diffusion. The filter
// uses a level set formulation where the iso-intensity contours in an image
// are viewed as level sets, where pixels of a particular intensity form one
// level set. The level set function is then evolved under the control of
// a diffusion equation where the speed is proportional to the
// curvature of the contour:
//
// \begin{equation}
// I_t = \kappa |\nabla I|
// \end{equation}
//
// where $ \kappa $ is the curvature.
//
// Areas of high curvature will diffuse faster than areas of low curvature.
// Hence, small jagged noise artifacts will disappear quickly, while large
// scale interfaces will be slow to evolve, thereby preserving sharp
// boundaries between objects. However, it should be noted that although the
// evolution at the boundary is slow, some diffusion will still occur. Thus,
// continual application of this curvature flow scheme will eventually
// result in the removal of information as each contour shrinks to a point
// and disappears.
//
// \index{itk::CurvatureFlowImageFilter}
//
// 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::CurvatureFlowImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
int
main(int argc, char * argv[])
{
if (argc < 5)
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0]
<< " inputImageFile outputImageFile numberOfIterations timeStep"
<< std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be selected based on the pixel types required for the
// input and output images.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InputPixelType = float;
using OutputPixelType = float;
// 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, 2>;
using OutputImageType = itk::Image<OutputPixelType, 2>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The CurvatureFlow filter type is now instantiated using both the
// input image and the output image types.
//
// \index{itk::CurvatureFlowImageFilter!instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
ReaderType::Pointer 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::CurvatureFlowImageFilter!New()}
// \index{itk::CurvatureFlowImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FilterType::Pointer 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 timeStep = std::stod(argv[4]);
// Software Guide : BeginLatex
//
// The CurvatureFlow filter requires two parameters: the number of
// iterations to be performed and the time step used in the computation of
// the level set evolution. These two parameters are set using the methods
// \code{SetNumberOfIterations()} and \code{SetTimeStep()} respectively.
// Then the filter can be executed by invoking \code{Update()}.
//
// \index{itk::CurvatureFlowImageFilter!Update()}
// \index{itk::CurvatureFlowImageFilter!SetTimeStep()}
// \index{itk::CurvatureFlowImageFilter!SetNumberOfIterations()}
// \index{SetTimeStep()!itk::CurvatureFlowImageFilter}
// \index{SetNumberOfIterations()!itk::CurvatureFlowImageFilter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNumberOfIterations(numberOfIterations);
filter->SetTimeStep(timeStep);
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
// $10$, more iterations will result in further smoothing and will
// increase the computing time linearly. Edge-preserving behavior is not
// guaranteed by this filter. Some degradation will occur on the edges and
// will increase as the number of iterations is increased.
//
// 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
// trigger the execution of this one. For example, a writer filter could
// be used after the curvature flow filter.
//
// Software Guide : EndLatex
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]);
// Software Guide : BeginCodeSnippet
rescaler->SetInput(filter->GetOutput());
writer->SetInput(rescaler->GetOutput());
writer->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{CurvatureFlowImageFilterOutput}
// \itkcaption[CurvatureFlowImageFilter output]{Effect of the
// CurvatureFlowImageFilter on a slice from a MRI proton density image of
// the brain.}
// \label{fig:CurvatureFlowImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:CurvatureFlowImageFilterInputOutput} 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 $10$
// iterations. The figure shows how homogeneous regions are smoothed and
// edges are preserved.
//
// \relatedClasses
// \begin{itemize}
// \item \doxygen{GradientAnisotropicDiffusionImageFilter}
// \item \doxygen{CurvatureAnisotropicDiffusionImageFilter}
// \item \doxygen{BilateralImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
itk::CurvatureFlowImageFilter
Denoise an image using curvature driven flow.
Definition: itkCurvatureFlowImageFilter.h:95
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
itkCurvatureFlowImageFilter.h
itkRescaleIntensityImageFilter.h
itkImageFileWriter.h
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:153
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86