ITK  4.8.0 Insight Segmentation and Registration Toolkit
Examples/Filtering/MinMaxCurvatureFlowImageFilter.cxx
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// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {MinMaxCurvatureFlowImageFilterOutput.png}
// ARGUMENTS: 10 0.125 1
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
//
// \begin{figure}
// \center
// \includegraphics[width=0.5\textwidth]{MinMaxCurvatureFlowFunctionDiagram}
// \itkcaption[MinMaxCurvatureFlow computation]{Elements involved in the
// computation of min-max curvature flow.}
// \label{fig:MinMaxCurvatureFlowFunctionDiagram}
// \end{figure}
//
// The MinMax curvature flow filter applies a variant of the curvature flow
// algorithm where diffusion is turned on or off depending of the scale of the
// noise that one wants to remove. The evolution speed is switched between
// $\min(\kappa,0)$ and $\max(\kappa,0)$ such that:
//
//
// I_t = F |\nabla I|
//
//
// where $F$ is defined as
//
//
// \max(\kappa,0) & \mbox{Average} < Threshold \\ \min(\kappa,0) & \mbox{Average} \ge Threshold
// \end{array} \right.
//
//
// The $Average$ is the average intensity computed over a neighborhood of a
// user-specified radius of the pixel. The choice of the radius governs the
// scale of the noise to be removed. The $Threshold$ is calculated as the
// average of pixel intensities along the direction perpendicular to the
// gradient at the \emph{extrema} of the local neighborhood.
//
// A speed of $F = max(\kappa,0)$ will cause small dark regions in a
// predominantly light region to shrink. Conversely, a speed of $F = // min(\kappa,0)$, will cause light regions in a predominantly dark region to
// shrink. Comparison between the neighborhood average and the threshold is
// used to select the the right speed function to use. This switching
// prevents the unwanted diffusion of the simple curvature flow method.
//
// Figure~\ref{fig:MinMaxCurvatureFlowFunctionDiagram} shows the main
// elements involved in the computation. The set of square pixels represent
// the neighborhood over which the average intensity is being computed. The
// gray pixels are those lying close to the direction perpendicular to the
// gradient. The pixels which intersect the neighborhood bounds are used to
// compute the threshold value in the equation above. The integer radius of
// the neighborhood is selected by the user.
//
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter}
//
// Software Guide : EndLatex
#include "itkImage.h"
// Software Guide : BeginLatex
//
// The first step required to use the \doxygen{MinMaxCurvatureFlowImageFilter}
// 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 stencilRadius" << 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 input and output image types are
// instantiated.
//
// 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 \doxygen{MinMaxCurvatureFlowImageFilter} type is now instantiated
// using both the input image and the output image types. The filter is
// then created using the \code{New()} method.
//
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter!instantiation}
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter!New()}
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
InputImageType, OutputImageType > 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.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
const unsigned int numberOfIterations = atoi( argv[3] );
const double timeStep = atof( argv[4] );
// Software Guide : BeginLatex
//
// The \doxygen{MinMaxCurvatureFlowImageFilter} requires the two normal
// parameters of the CurvatureFlow image, the number of iterations to be
// performed and the time step used in the computation of the level set
// required. This last parameter is passed using the
// integer number since it is referring to a number of pixels from the center
// to the border of the neighborhood. Then the filter can be executed by
// invoking \code{Update()}.
//
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter!Update()}
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter!SetTimeStep()}
// \index{itk::MinMax\-Curvature\-Flow\-Image\-Filter!SetNumberOfIterations()}
// \index{SetTimeStep()!itk::MinMax\-Curvature\-Flow\-Image\-Filter}
// \index{SetNumberOfIterations()!itk::MinMax\-Curvature\-Flow\-Image\-Filter}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetTimeStep( timeStep );
filter->SetNumberOfIterations( numberOfIterations );
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. The radius of the stencil can be
// typically $1$. The \emph{edge-preserving} characteristic is not perfect
// on 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 can
// be used after the curvature flow filter.
//
// Software Guide : EndLatex
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] );
// 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]{MinMaxCurvatureFlowImageFilterOutput}
// \itkcaption[MinMaxCurvatureFlowImageFilter output]{Effect of the
// MinMaxCurvatureFlowImageFilter on a slice from a MRI proton density image
// of the brain.}
// \label{fig:MinMaxCurvatureFlowImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:MinMaxCurvatureFlowImageFilterInputOutput} 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 $0.125$,
// $10$ iterations and a radius of $1$. The figure shows how homogeneous
// regions are smoothed and edges are preserved. Notice also, that the
// result in the figure has sharper edges than the same example using
// simple curvature flow in Figure
// \ref{fig:CurvatureFlowImageFilterInputOutput}.
//
// \relatedClasses
// \begin{itemize}
// \item \doxygen{CurvatureFlowImageFilter}
// \end{itemize}
//
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}