[Insight-users] Running time for a level set segmentation method
john smith
mkitkinsightuser at gmail.com
Tue Apr 12 07:17:39 EDT 2011
Here is the code for "GeodesicActiveContourImageFilter". It is quite similar
with the "ShapeDetectionLevelSetFilter". I took it drom the software guide
for itk, but I use it for 3D image.
////////// code /////////////////
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: GeodesicActiveContourImageFilter.cxx,v $
Language: C++
Date: $Date: 2009-03-17 21:44:42 $
Version: $Revision: 1.45 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#ifdef __BORLANDC__
#define ITK_LEAN_AND_MEAN
#endif
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {GeodesicActiveContourImageFilterOutput5.png}
// 81 114 5 1.0 -0.5 3.0 2
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {GeodesicActiveContourImageFilterOutput6.png}
// 99 114 5 1.0 -0.5 3.0 2
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {GeodesicActiveContourImageFilterOutput8.png}
// 40 90 5 0.5 -0.3 2.0 10
// Software Guide : EndCommandLineArgs
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {GeodesicActiveContourImageFilterOutput7.png}
// OUTPUTS: [GeodesicActiveContourImageFilterOutput1.png]
// OUTPUTS: [GeodesicActiveContourImageFilterOutput2.png]
// OUTPUTS: [GeodesicActiveContourImageFilterOutput3.png]
// 56 92 5 1.0 -0.3 2.0 10
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The use of the \doxygen{GeodesicActiveContourLevelSetImageFilter} is
// illustrated in the following example. The implementation of this filter
in
// ITK is based on the paper by Caselles \cite{Caselles1997}. This
// implementation extends the functionality of the
// \doxygen{ShapeDetectionLevelSetImageFilter} by the addition of a third
// advection term which attracts the level set to the object boundaries.
//
// GeodesicActiveContourLevelSetImageFilter expects two inputs. The first
is
// an initial level set in the form of an \doxygen{Image}. The second input
// is a feature image. For this algorithm, the feature image is an edge
// potential image that basically follows the same rules used for the
// ShapeDetectionLevelSetImageFilter discussed in
// Section~\ref{sec:ShapeDetectionLevelSetFilter}. The configuration of
this
// example is quite similar to the example on the use of the
// ShapeDetectionLevelSetImageFilter. We omit most of the redundant
// description. A look at the code will reveal the great degree of
similarity
// between both examples.
//
// \begin{figure} \center
//
\includegraphics[width=\textwidth]{GeodesicActiveContoursCollaborationDiagram1.eps}
// \itkcaption[GeodesicActiveContourLevelSetImageFilter collaboration
// diagram]{Collaboration diagram for the
GeodesicActiveContourLevelSetImageFilter
// applied to a segmentation task.}
// \label{fig:GeodesicActiveContoursCollaborationDiagram}
// \end{figure}
//
// Figure~\ref{fig:GeodesicActiveContoursCollaborationDiagram} shows the
major
// components involved in the application of the
// GeodesicActiveContourLevelSetImageFilter to a segmentation task.
// This pipeline is quite similar to the one used by the
// ShapeDetectionLevelSetImageFilter in
// section~\ref{sec:ShapeDetectionLevelSetFilter}.
//
// The pipeline involves a first stage of smoothing using the
// \doxygen{CurvatureAnisotropicDiffusionImageFilter}. The smoothed image is
// passed as the input to the
// \doxygen{GradientMagnitudeRecursiveGaussianImageFilter} and then to the
// \doxygen{SigmoidImageFilter} in order to produce the edge potential
image.
// A set of user-provided seeds is passed to a
// \doxygen{FastMarchingImageFilter} in order to compute the distance map. A
// constant value is subtracted from this map in order to obtain a level set
// in which the \emph{zero set} represents the initial contour. This level
// set is also passed as input to the
// GeodesicActiveContourLevelSetImageFilter.
//
// Finally, the level set generated by the
// GeodesicActiveContourLevelSetImageFilter is passed to a
// \doxygen{BinaryThresholdImageFilter} in order to produce a binary mask
// representing the segmented object.
//
// Let's start by including the headers of the main filters involved in the
// preprocessing.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
#include "itkGeodesicActiveContourLevelSetImageFilter.h"
// Software Guide : EndCodeSnippet
#include "itkCurvatureAnisotropicDiffusionImageFilter.h"
#include "itkGradientMagnitudeRecursiveGaussianImageFilter.h"
#include "itkSigmoidImageFilter.h"
#include "itkFastMarchingImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkBinaryThresholdImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
int main( int argc, char *argv[] )
{
if( argc < 11 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " inputImage outputImage";
std::cerr << " seedX seedY seedZ InitialDistance";
std::cerr << " Sigma SigmoidAlpha SigmoidBeta";
std::cerr << " PropagationScaling" << std::endl;
return 1;
}
// Software Guide : BeginLatex
//
// We now define the image type using a particular pixel type and
// dimension. In this case the \code{float} type is used for the pixels
// due to the requirements of the smoothing filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef float InternalPixelType;
const unsigned int Dimension = 3;
typedef itk::Image< InternalPixelType, Dimension > InternalImageType;
// Software Guide : EndCodeSnippet
// The following lines instantiate the thresholding filter that will
// process the final level set at the output of the
// GeodesicActiveContourLevelSetImageFilter.
//
typedef unsigned char OutputPixelType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::BinaryThresholdImageFilter<
InternalImageType,
OutputImageType > ThresholdingFilterType;
ThresholdingFilterType::Pointer thresholder =
ThresholdingFilterType::New();
thresholder->SetLowerThreshold( -1000.0 );
thresholder->SetUpperThreshold( 0.0 );
thresholder->SetOutsideValue( 0 );
thresholder->SetInsideValue( 255 );
// We instantiate reader and writer types in the following lines.
//
typedef itk::ImageFileReader< InternalImageType > ReaderType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
ReaderType::Pointer reader = ReaderType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName( argv[1] );
writer->SetFileName( argv[2] );
// The RescaleIntensityImageFilter type is declared below. This filter
will
// renormalize image before sending them to writers.
//
typedef itk::RescaleIntensityImageFilter<
InternalImageType,
OutputImageType > CastFilterType;
// The \doxygen{CurvatureAnisotropicDiffusionImageFilter} type is
// instantiated using the internal image type.
//
typedef itk::CurvatureAnisotropicDiffusionImageFilter<
InternalImageType,
InternalImageType > SmoothingFilterType;
SmoothingFilterType::Pointer smoothing = SmoothingFilterType::New();
// The types of the
// GradientMagnitudeRecursiveGaussianImageFilter and
// SigmoidImageFilter are instantiated using the internal image
// type.
//
typedef itk::GradientMagnitudeRecursiveGaussianImageFilter<
InternalImageType,
InternalImageType > GradientFilterType;
typedef itk::SigmoidImageFilter<
InternalImageType,
InternalImageType > SigmoidFilterType;
GradientFilterType::Pointer gradientMagnitude =
GradientFilterType::New();
SigmoidFilterType::Pointer sigmoid = SigmoidFilterType::New();
// The minimum and maximum values of the SigmoidImageFilter output
// are defined with the methods \code{SetOutputMinimum()} and
// \code{SetOutputMaximum()}. In our case, we want these two values to be
// $0.0$ and $1.0$ respectively in order to get a nice speed image to
feed
// the \code{FastMarchingImageFilter}. Additional details on the user of
the
// \doxygen{SigmoidImageFilter} are presented in
// section~\ref{sec:IntensityNonLinearMapping}.
sigmoid->SetOutputMinimum( 0.0 );
sigmoid->SetOutputMaximum( 1.0 );
// We declare now the type of the FastMarchingImageFilter that
// will be used to generate the initial level set in the form of a
distance
// map.
//
typedef itk::FastMarchingImageFilter<
InternalImageType,
InternalImageType > FastMarchingFilterType;
// Next we construct one filter of this class using the \code{New()}
// method.
//
FastMarchingFilterType::Pointer fastMarching =
FastMarchingFilterType::New();
// Software Guide : BeginLatex
//
// In the following lines we instantiate the type of the
// GeodesicActiveContourLevelSetImageFilter and create an object of this
// type using the \code{New()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::GeodesicActiveContourLevelSetImageFilter< InternalImageType,
InternalImageType > GeodesicActiveContourFilterType;
GeodesicActiveContourFilterType::Pointer geodesicActiveContour =
GeodesicActiveContourFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// For the GeodesicActiveContourLevelSetImageFilter, scaling parameters
// are used to trade off between the propagation (inflation), the
// curvature (smoothing) and the advection terms. These parameters are
set
// using methods \code{SetPropagationScaling()},
// \code{SetCurvatureScaling()} and \code{SetAdvectionScaling()}. In this
// example, we will set the curvature and advection scales to one and let
// the propagation scale be a command-line argument.
//
//
\index{itk::Geodesic\-Active\-Contour\-LevelSet\-Image\-Filter!SetPropagationScaling()}
//
\index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetPropagationScaling()}
//
\index{itk::Geodesic\-Active\-Contour\-LevelSet\-Image\-Filter!SetCurvatureScaling()}
//
\index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetCurvatureScaling()}
//
\index{itk::Geodesic\-Active\-Contour\-LevelSet\-Image\-Filter!SetAdvectionScaling()}
//
\index{itk::Segmentation\-Level\-Set\-Image\-Filter!SetAdvectionScaling()}
//
// Software Guide : EndLatex
const double propagationScaling = atof( argv[10] );
// Software Guide : BeginCodeSnippet
geodesicActiveContour->SetPropagationScaling( propagationScaling );
geodesicActiveContour->SetCurvatureScaling( 1.0 );
geodesicActiveContour->SetAdvectionScaling( 1.0 );
// Software Guide : EndCodeSnippet
// Once activiated the level set evolution will stop if the convergence
// criteria or if the maximum number of iterations is reached. The
// convergence criteria is defined in terms of the root mean squared
(RMS)
// change in the level set function. The evolution is said to have
// converged if the RMS change is below a user specified threshold. In a
// real application is desirable to couple the evolution of the zero set
// to a visualization module allowing the user to follow the evolution of
// the zero set. With this feedback, the user may decide when to stop the
// algorithm before the zero set leaks through the regions of low
gradient
// in the contour of the anatomical structure to be segmented.
geodesicActiveContour->SetMaximumRMSError( 0.02 );
geodesicActiveContour->SetNumberOfIterations( 800 );
// Software Guide : BeginLatex
//
// The filters are now connected in a pipeline indicated in
// Figure~\ref{fig:GeodesicActiveContoursCollaborationDiagram} using the
// following lines:
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
smoothing->SetInput( reader->GetOutput() );
gradientMagnitude->SetInput( smoothing->GetOutput() );
sigmoid->SetInput( gradientMagnitude->GetOutput() );
geodesicActiveContour->SetInput( fastMarching->GetOutput() );
geodesicActiveContour->SetFeatureImage( sigmoid->GetOutput() );
thresholder->SetInput( geodesicActiveContour->GetOutput() );
writer->SetInput( thresholder->GetOutput() );
// Software Guide : EndCodeSnippet
// The CurvatureAnisotropicDiffusionImageFilter requires a couple of
// parameter to be defined. The following are typical values for $2D$
// images. However they may have to be adjusted depending on the amount
of
// noise present in the input image. This filter has been discussed in
// section~\ref{sec:GradientAnisotropicDiffusionImageFilter}.
smoothing->SetTimeStep( 0.0625 );
smoothing->SetNumberOfIterations( 5 );
smoothing->SetConductanceParameter( 9.0 );
// The GradientMagnitudeRecursiveGaussianImageFilter performs the
// equivalent of a convolution with a Gaussian kernel, followed by a
// derivative operator. The sigma of this Gaussian can be used to control
// the range of influence of the image edges. This filter has been
discussed
// in Section~\ref{sec:GradientMagnitudeRecursiveGaussianImageFilter}
const double sigma = atof( argv[7] );
gradientMagnitude->SetSigma( sigma );
// The SigmoidImageFilter requires two parameters that define the linear
// transformation to be applied to the sigmoid argument. This parameters
// have been discussed in Sections~\ref{sec:IntensityNonLinearMapping}
and
// \ref{sec:FastMarchingImageFilter}.
const double alpha = atof( argv[8] );
const double beta = atof( argv[9] );
sigmoid->SetAlpha( alpha );
sigmoid->SetBeta( beta );
// The FastMarchingImageFilter requires the user to provide a seed
// point from which the level set will be generated. The user can
actually
// pass not only one seed point but a set of them. Note the the
// FastMarchingImageFilter is used here only as a helper in the
// determination of an initial level set. We could have used the
// \doxygen{DanielssonDistanceMapImageFilter} in the same way.
//
// The seeds are passed stored in a container. The type of this
// container is defined as \code{NodeContainer} among the
// FastMarchingImageFilter traits.
//
typedef FastMarchingFilterType::NodeContainer NodeContainer;
typedef FastMarchingFilterType::NodeType NodeType;
NodeContainer::Pointer seeds = NodeContainer::New();
InternalImageType::IndexType seedPosition;
seedPosition[0] = atoi( argv[3] );
seedPosition[1] = atoi( argv[4] );
seedPosition[2] = atoi( argv[5] );
// Nodes are created as stack variables and initialized with a value and
an
// \doxygen{Index} position. Note that here we assign the value of minus
the
// user-provided distance to the unique node of the seeds passed to the
// FastMarchingImageFilter. In this way, the value will increment
// as the front is propagated, until it reaches the zero value
corresponding
// to the contour. After this, the front will continue propagating until
it
// fills up the entire image. The initial distance is taken here from the
// command line arguments. The rule of thumb for the user is to select
this
// value as the distance from the seed points at which she want the
initial
// contour to be.
const double initialDistance = atof( argv[6] );
NodeType node;
const double seedValue = - initialDistance;
node.SetValue( seedValue );
node.SetIndex( seedPosition );
// The list of nodes is initialized and then every node is inserted using
// the \code{InsertElement()}.
seeds->Initialize();
seeds->InsertElement( 0, node );
// The set of seed nodes is passed now to the
// FastMarchingImageFilter with the method
// \code{SetTrialPoints()}.
//
fastMarching->SetTrialPoints( seeds );
// Since the FastMarchingImageFilter is used here just as a
// Distance Map generator. It does not require a speed image as input.
// Instead the constant value $1.0$ is passed using the
// \code{SetSpeedConstant()} method.
//
fastMarching->SetSpeedConstant( 1.0 );
// Here we configure all the writers required to see the intermediate
// outputs of the pipeline. This is added here only for
// pedagogical/debugging purposes. These intermediate output are normaly
not
// required. Only the output of the final thresholding filter should be
// relevant. Observing intermediate output is helpful in the process of
// fine tuning the parameters of filters in the pipeline.
//
CastFilterType::Pointer caster1 = CastFilterType::New();
CastFilterType::Pointer caster2 = CastFilterType::New();
CastFilterType::Pointer caster3 = CastFilterType::New();
CastFilterType::Pointer caster4 = CastFilterType::New();
WriterType::Pointer writer1 = WriterType::New();
WriterType::Pointer writer2 = WriterType::New();
WriterType::Pointer writer3 = WriterType::New();
WriterType::Pointer writer4 = WriterType::New();
caster1->SetInput( smoothing->GetOutput() );
writer1->SetInput( caster1->GetOutput() );
writer1->SetFileName("GeodesicActiveContourImageFilterOutput1.hdr");
caster1->SetOutputMinimum( 0 );
caster1->SetOutputMaximum( 255 );
writer1->Update();
caster2->SetInput( gradientMagnitude->GetOutput() );
writer2->SetInput( caster2->GetOutput() );
writer2->SetFileName("GeodesicActiveContourImageFilterOutput2.hdr");
caster2->SetOutputMinimum( 0 );
caster2->SetOutputMaximum( 255 );
writer2->Update();
caster3->SetInput( sigmoid->GetOutput() );
writer3->SetInput( caster3->GetOutput() );
writer3->SetFileName("GeodesicActiveContourImageFilterOutput3.hdr");
caster3->SetOutputMinimum( 0 );
caster3->SetOutputMaximum( 255 );
writer3->Update();
caster4->SetInput( fastMarching->GetOutput() );
writer4->SetInput( caster4->GetOutput() );
writer4->SetFileName("GeodesicActiveContourImageFilterOutput4.hdr");
caster4->SetOutputMinimum( 0 );
caster4->SetOutputMaximum( 255 );
// The FastMarchingImageFilter requires the user to specify the
// size of the image to be produced as output. This is done using the
// \code{SetOutputSize()}. Note that the size is obtained here from the
// output image of the smoothing filter. The size of this image is valid
// only after the \code{Update()} methods of this filter has been called
// directly or indirectly.
//
fastMarching->SetOutputSize(
reader->GetOutput()->GetBufferedRegion().GetSize() );
// Software Guide : BeginLatex
//
// The invocation of the \code{Update()} method on the writer triggers
the
// execution of the pipeline. As usual, the call is placed in a
// \code{try/catch} block should any errors occur or exceptions be
thrown.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
writer->Update();
}
catch( itk::ExceptionObject & excep )
{
std::cerr << "Exception caught !" << std::endl;
std::cerr << excep << std::endl;
}
// Software Guide : EndCodeSnippet
// Print out some useful information
std::cout << std::endl;
std::cout << "Max. no. iterations: " <<
geodesicActiveContour->GetNumberOfIterations() << std::endl;
std::cout << "Max. RMS error: " <<
geodesicActiveContour->GetMaximumRMSError() << std::endl;
std::cout << std::endl;
std::cout << "No. elpased iterations: " <<
geodesicActiveContour->GetElapsedIterations() << std::endl;
std::cout << "RMS change: " << geodesicActiveContour->GetRMSChange() <<
std::endl;
writer4->Update();
// The following writer type is used to save the output of the
time-crossing
// map in a file with apropiate pixel representation. The advantage of
saving
// this image in native format is that it can be used with a viewer to
help
// determine an appropriate threshold to be used on the output of the
// fastmarching filter.
//
// typedef itk::ImageFileWriter< InternalImageType > InternalWriterType;
// InternalWriterType::Pointer mapWriter = InternalWriterType::New();
// mapWriter->SetInput( fastMarching->GetOutput() );
// mapWriter->SetFileName("GeodesicActiveContourImageFilterOutput4.mha");
// mapWriter->Update();
// InternalWriterType::Pointer speedWriter = InternalWriterType::New();
// speedWriter->SetInput( sigmoid->GetOutput() );
// speedWriter->SetFileName("GeodesicActiveContourImageFilterOutput3.mha");
// speedWriter->Update();
// InternalWriterType::Pointer gradientWriter = InternalWriterType::New();
// gradientWriter->SetInput( gradientMagnitude->GetOutput() );
//
gradientWriter->SetFileName("GeodesicActiveContourImageFilterOutput2.mha");
// gradientWriter->Update();
// Software Guide : BeginLatex
//
// Let's now run this example using as input the image
// \code{BrainProtonDensitySlice.png} provided in the directory
// \code{Examples/Data}. We can easily segment the major anatomical
// structures by providing seeds in the appropriate locations.
// Table~\ref{tab:GeodesicActiveContourImageFilterOutput2} presents the
// parameters used for some structures.
//
// \begin{table}
// \begin{center}
// \begin{tabular}{|l|c|c|c|c|c|c|c|c|}
// \hline
// Structure & Seed Index & Distance & $\sigma$ &
// $\alpha$ & $\beta$ & Propag. & Output Image \\ \hline
// Left Ventricle & $(81,114)$ & 5.0 & 1.0 & -0.5 & 3.0 & 2.0 &
First \\ \hline
// Right Ventricle & $(99,114)$ & 5.0 & 1.0 & -0.5 & 3.0 & 2.0 &
Second \\ \hline
// White matter & $(56, 92)$ & 5.0 & 1.0 & -0.3 & 2.0 & 10.0 &
Third \\ \hline
// Gray matter & $(40, 90)$ & 5.0 & 0.5 & -0.3 & 2.0 & 10.0 &
Fourth \\ \hline
// \end{tabular}
// \end{center}
// \itkcaption[GeodesicActiveContour segmentation example
parameters]{Parameters used
// for segmenting some brain structures shown in
// Figure~\ref{fig:GeodesicActiveContourImageFilterOutput2} using the
filter
// GeodesicActiveContourLevelSetImageFilter.
// \label{tab:GeodesicActiveContourImageFilterOutput2}}
// \end{table}
//
// Figure~\ref{fig:GeodesicActiveContourImageFilterOutput} presents the
// intermediate outputs of the pipeline illustrated in
// Figure~\ref{fig:GeodesicActiveContoursCollaborationDiagram}. They are
// from left to right: the output of the anisotropic diffusion filter,
the
// gradient magnitude of the smoothed image and the sigmoid of the
gradient
// magnitude which is finally used as the edge potential for the
// GeodesicActiveContourLevelSetImageFilter.
//
// \begin{figure} \center
// \includegraphics[height=0.40\textheight]{BrainProtonDensitySlice.eps}
//
\includegraphics[height=0.40\textheight]{GeodesicActiveContourImageFilterOutput1.eps}
//
\includegraphics[height=0.40\textheight]{GeodesicActiveContourImageFilterOutput2.eps}
//
\includegraphics[height=0.40\textheight]{GeodesicActiveContourImageFilterOutput3.eps}
// \itkcaption[GeodesicActiveContourLevelSetImageFilter intermediate
// output]{Images generated by the segmentation process based on the
// GeodesicActiveContourLevelSetImageFilter. From left to right and top to
// bottom: input image to be segmented, image smoothed with an
// edge-preserving smoothing filter, gradient magnitude of the smoothed
// image, sigmoid of the gradient magnitude. This last image, the sigmoid,
is
// used to compute the speed term for the front propagation.}
// \label{fig:GeodesicActiveContourImageFilterOutput} \end{figure}
//
// Segmentations of the main brain structures are presented in
// Figure~\ref{fig:GeodesicActiveContourImageFilterOutput2}. The results
// are quite similar to those obtained with the
// ShapeDetectionLevelSetImageFilter in
// Section~\ref{sec:ShapeDetectionLevelSetFilter}.
//
// Note that a relatively larger propagation scaling value was required
to
// segment the white matter. This is due to two factors: the lower
// contrast at the border of the white matter and the complex shape of
the
// structure. Unfortunately the optimal value of these scaling parameters
// can only be determined by experimentation. In a real application we
// could imagine an interactive mechanism by which a user supervises the
// contour evolution and adjusts these parameters accordingly.
//
// \begin{figure} \center
//
\includegraphics[width=0.24\textwidth]{GeodesicActiveContourImageFilterOutput5.eps}
//
\includegraphics[width=0.24\textwidth]{GeodesicActiveContourImageFilterOutput6.eps}
//
\includegraphics[width=0.24\textwidth]{GeodesicActiveContourImageFilterOutput7.eps}
//
\includegraphics[width=0.24\textwidth]{GeodesicActiveContourImageFilterOutput8.eps}
// \itkcaption[GeodesicActiveContourImageFilter segmentations]{Images
generated by the
// segmentation process based on the GeodesicActiveContourImageFilter.
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