ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/Filtering/VectorCurvatureAnisotropicDiffusionImageFilter.cxx
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// Software Guide : BeginLatex
//
// The \doxygen{VectorCurvatureAnisotropicDiffusionImageFilter} performs
// anisotropic diffusion on a vector image using a modified curvature
// diffusion equation (MCDE). The MCDE is the same described in
// \ref{sec:CurvatureAnisotropicDiffusionImageFilter}.
//
// Typically in vector-valued diffusion, vector components are diffused
// independently of one another using a conductance term that is linked across
// the components.
//
// This filter is designed to process images of \doxygen{Vector} type. The
// code relies on various typedefs and overloaded operators defined in
// \doxygen{Vector}. It is perfectly reasonable, however, to apply this
// filter to images of other, user-defined types as long as the appropriate
// typedefs and operator overloads are in place. As a general rule, follow
// the example of the \doxygen{Vector} class in defining your data types.
//
// \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The first step required to use 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 outputGradientImageFile ";
std::cerr << "numberOfIterations timeStep " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be selected based on required pixel type 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 itk::CovariantVector< float, 2 > VectorPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< VectorPixelType, 2 > VectorImageType;
// 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::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!instantiation}
// \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!New()}
// \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
VectorImageType, VectorImageType > FilterType;
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 and its data is passed through a
// gradient filter in order to generate an image of vectors.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
const unsigned int numberOfIterations = atoi( argv[4] );
const double timeStep = atof( argv[5] );
// Software Guide : BeginLatex
//
// This 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 parameters are set using the methods
// \code{SetNumberOfIterations()} and \code{SetTimeStep()} respectively.
// The filter can be executed by invoking \code{Update()}.
//
// \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!Update()}
// \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetTimeStep()}
// \index{itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter!SetNumberOfIterations()}
// \index{SetTimeStep()!itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
// \index{SetNumberOfIterations()!itk::Vector\-Curvature\-Anisotropic\-Diffusion\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetNumberOfIterations( numberOfIterations );
filter->SetTimeStep( timeStep );
filter->SetConductanceParameter(1.0);
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
// $5$, however more iterations will result in further smoothing and will
// increase the computing time linearly.
//
// 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 float OutputPixelType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
VectorImageType, OutputImageType > ComponentFilterType;
ComponentFilterType::Pointer component = ComponentFilterType::New();
// Select the component to extract.
component->SetIndex( 0 );
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();
rescaler->SetInput( component->GetOutput() );
writer->SetInput( rescaler->GetOutput() );
// Save the component of the original gradient
writer->SetFileName( argv[2] );
writer->Update();
// Save the component of the smoothed gradient
component->SetInput( filter->GetOutput() );
writer->SetFileName( argv[3] );
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{VectorCurvatureAnisotropicDiffusionImageFilterInput}
// \includegraphics[width=0.44\textwidth]{VectorCurvatureAnisotropicDiffusionImageFilterOutput}
// \itkcaption[VectorCurvatureAnisotropicDiffusionImageFilter output]{Effect
// of the VectorCurvatureAnisotropicDiffusionImageFilter on the $X$ component
// of the gradient from a MRIproton density brain image.}
// \label{fig:VectorCurvatureAnisotropicDiffusionImageFilterInputOutput}
// \end{figure}
//
// Figure~\ref{fig:VectorCurvatureAnisotropicDiffusionImageFilterInputOutput}
// illustrates the effect of this filter on a MRI proton density image of
// the brain. The images show the $X$ component of the gradient before
// (left) and after (right) the application of the filter. In this example
// the filter was run with a time step of 0.25, and 5 iterations.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}