[ITK-users] SimpleITK for Java Image Fusion and Co-registration?
Lowekamp, Bradley (NIH/NLM/LHC) [C]
blowekamp at mail.nih.gov
Thu Sep 29 10:56:53 EDT 2016
I was just working on translating the ImageRegistration1 example to Java. How does this work:
http://review.source.kitware.com/#/c/21596/1/Examples/Java/ImageRegistrationMethod1.java
Brad
> On Sep 29, 2016, at 9:30 AM, ivan <ivan.granata.na at gmail.com> wrote:
>
> Please Can anyone help me to translate this algorithm in Java?
> i must write a Co-registration with SimpleITK in Java
>
>
>
> import SimpleITK as sitk
>
> #read the images
> fixed_image = sitk.ReadImage('training_001_ct.mha', sitk.sitkFloat32)
> moving_image = sitk.ReadImage('training_001_mr_T1.mha', sitk.sitkFloat32)
>
> #initial alignment of the two volumes
> transform = sitk.CenteredTransformInitializer(fixed_image,
> moving_image,
> sitk.Euler3DTransform(),
>
> sitk.CenteredTransformInitializerFilter.GEOMETRY)
>
> #multi-resolution rigid registration using Mutual Information
> registration_method = sitk.ImageRegistrationMethod()
> registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
> registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)
> registration_method.SetMetricSamplingPercentage(0.01)
> registration_method.SetInterpolator(sitk.sitkLinear)
> registration_method.SetOptimizerAsGradientDescent(learningRate=1.0,
> numberOfIterations=100,
>
> convergenceMinimumValue=1e-6,
> convergenceWindowSize=10)
> registration_method.SetOptimizerScalesFromPhysicalShift()
> registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [4,2,1])
> registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[2,1,0])
> registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()
> registration_method.SetInitialTransform(transform)
> registration_method.Execute(fixed_image, moving_image)
>
>
> OR DETAILED, THIS ONE
>
>
>
>
>
> #include "itkImageRegistrationMethodv4.h"
> #include "itkTranslationTransform.h"
> #include "itkMattesMutualInformationImageToImageMetricv4.h"
> #include "itkRegularStepGradientDescentOptimizerv4.h"
>
>
>
> #include "itkImageFileReader.h"
> #include "itkImageFileWriter.h"
>
> #include "itkResampleImageFilter.h"
> #include "itkCastImageFilter.h"
> #include "itkCheckerBoardImageFilter.h"
>
>
> // The following section of code implements a Command observer
> // used to monitor the evolution of the registration process.
> //
> #include "itkCommand.h"
> class CommandIterationUpdate : public itk::Command
> {
> public:
> typedef CommandIterationUpdate Self;
> typedef itk::Command Superclass;
> typedef itk::SmartPointer<Self> Pointer;
> itkNewMacro( Self );
>
> protected:
> CommandIterationUpdate() {};
>
> public:
> typedef itk::RegularStepGradientDescentOptimizerv4<double> OptimizerType;
> typedef const OptimizerType *
> OptimizerPointer;
>
> void Execute(itk::Object *caller, const itk::EventObject & event)
> ITK_OVERRIDE
> {
> Execute( (const itk::Object *)caller, event);
> }
>
> void Execute(const itk::Object * object, const itk::EventObject & event)
> ITK_OVERRIDE
> {
> OptimizerPointer optimizer = static_cast< OptimizerPointer >( object );
> if( ! itk::IterationEvent().CheckEvent( &event ) )
> {
> return;
> }
> std::cout << optimizer->GetCurrentIteration() << " ";
> std::cout << optimizer->GetValue() << " ";
> std::cout << optimizer->GetCurrentPosition() << std::endl;
> }
> };
>
> int main( int argc, char *argv[] )
> {
> if( argc < 4 )
> {
> std::cerr << "Missing Parameters " << std::endl;
> std::cerr << "Usage: " << argv[0];
> std::cerr << " fixedImageFile movingImageFile ";
> std::cerr << "outputImagefile [defaultPixelValue]" << std::endl;
> std::cerr << "[checkerBoardAfter] [checkerBoardBefore]" << std::endl;
> std::cerr << "[numberOfBins] [numberOfSamples]";
> std::cerr << "[useExplicitPDFderivatives ] " << std::endl;
> return EXIT_FAILURE;
> }
>
> const unsigned int Dimension = 2;
> typedef float PixelType;
>
> typedef itk::Image< PixelType, Dimension > FixedImageType;
> typedef itk::Image< PixelType, Dimension > MovingImageType;
>
> typedef itk::TranslationTransform< double, Dimension >
> TransformType;
> typedef itk::RegularStepGradientDescentOptimizerv4<double>
> OptimizerType;
> typedef itk::ImageRegistrationMethodv4<
> FixedImageType,
> MovingImageType,
> TransformType > RegistrationType;
>
> // Software Guide : BeginLatex
> //
> // In this example the image types and all registration components,
> // except the metric, are declared as in Section
> \ref{sec:IntroductionImageRegistration}.
> // The Mattes mutual information metric type is instantiated using the
> image types.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> typedef itk::MattesMutualInformationImageToImageMetricv4<
> FixedImageType,
> MovingImageType > MetricType;
> // Software Guide : EndCodeSnippet
>
> OptimizerType::Pointer optimizer = OptimizerType::New();
> RegistrationType::Pointer registration = RegistrationType::New();
>
> registration->SetOptimizer( optimizer );
>
>
> // Software Guide : BeginLatex
> //
> // The metric is created using the \code{New()} method and then
> // connected to the registration object.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> MetricType::Pointer metric = MetricType::New();
> registration->SetMetric( metric );
> // Software Guide : EndCodeSnippet
>
>
> // Software Guide : BeginLatex
> //
> // The metric requires the user to specify the number of bins
> // used to compute the entropy. In a typical application, 50 histogram
> bins
> // are sufficient. Note however, that the number of bins may have
> dramatic
> // effects on the optimizer's behavior.
> //
> //
> \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metricv4!SetNumberOfHistogramBins()}
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> unsigned int numberOfBins = 24;
> // Software Guide : EndCodeSnippet
>
> if( argc > 7 )
> {
> numberOfBins = atoi( argv[7] );
> }
>
>
> // Software Guide : BeginCodeSnippet
> metric->SetNumberOfHistogramBins( numberOfBins );
> // Software Guide : EndCodeSnippet
>
> // Software Guide : BeginLatex
> //
> // To calculate the image gradients, an image gradient calculator based
> on
> // ImageFunction is used instead of image gradient filters. Image
> gradient
> // methods are defined in the superclass \code{ImageToImageMetricv4}.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> metric->SetUseMovingImageGradientFilter( false );
> metric->SetUseFixedImageGradientFilter( false );
> // Software Guide : EndCodeSnippet
>
>
> typedef itk::ImageFileReader< FixedImageType > FixedImageReaderType;
> typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
>
> FixedImageReaderType::Pointer fixedImageReader =
> FixedImageReaderType::New();
> MovingImageReaderType::Pointer movingImageReader =
> MovingImageReaderType::New();
>
> fixedImageReader->SetFileName( argv[1] );
> movingImageReader->SetFileName( argv[2] );
>
> registration->SetFixedImage( fixedImageReader->GetOutput() );
> registration->SetMovingImage( movingImageReader->GetOutput() );
>
>
> // Software Guide : BeginLatex
> //
> // Notice that in the ITKv4 registration framework, optimizers always try
> // to minimize the cost function, and the metrics always return a
> parameter
> // and derivative result that improves the optimization, so this metric
> // computes the negative mutual information.
> // The optimization parameters are tuned for this example, so they are
> not
> // exactly the same as the parameters used in Section
> // \ref{sec:IntroductionImageRegistration}.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> optimizer->SetLearningRate( 8.00 );
> optimizer->SetMinimumStepLength( 0.001 );
> optimizer->SetNumberOfIterations( 200 );
> optimizer->ReturnBestParametersAndValueOn();
> // Software Guide : EndCodeSnippet
>
> // Software Guide : BeginLatex
> //
> // Note that large values of the learning rate will make the optimizer
> // unstable. Small values, on the other hand, may result in the optimizer
> // needing too many iterations in order to walk to the extrema of the cost
> // function. The easy way of fine tuning this parameter is to start with
> // small values, probably in the range of $\{1.0,5.0\}$. Once the other
> // registration parameters have been tuned for producing convergence, you
> // may want to revisit the learning rate and start increasing its value
> until
> // you observe that the optimization becomes unstable. The ideal value
> for
> // this parameter is the one that results in a minimum number of
> iterations
> // while still keeping a stable path on the parametric space of the
> // optimization. Keep in mind that this parameter is a multiplicative
> factor
> // applied on the gradient of the metric. Therefore, its effect on the
> // optimizer step length is proportional to the metric values themselves.
> // Metrics with large values will require you to use smaller values for
> the
> // learning rate in order to maintain a similar optimizer behavior.
> //
> // Whenever the regular step gradient descent optimizer encounters
> // change in the direction of movement in the parametric space, it reduces
> the
> // size of the step length. The rate at which the step length is reduced
> is
> // controlled by a relaxation factor. The default value of the factor is
> // $0.5$. This value, however may prove to be inadequate for noisy metrics
> // since they tend to induce erratic movements on the optimizers and
> // therefore result in many directional changes. In those
> // conditions, the optimizer will rapidly shrink the step length while it
> is
> // still too far from the location of the extrema in the cost function. In
> // this example we set the relaxation factor to a number higher than the
> // default in order to prevent the premature shrinkage of the step length.
> //
> //
> \index{itk::Regular\-Step\-Gradient\-Descent\-Optimizer!SetRelaxationFactor()}
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> optimizer->SetRelaxationFactor( 0.8 );
> // Software Guide : EndCodeSnippet
>
> // Create the Command observer and register it with the optimizer.
> //
> CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
> optimizer->AddObserver( itk::IterationEvent(), observer );
>
> // One level registration process without shrinking and smoothing.
> //
> const unsigned int numberOfLevels = 1;
>
> RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
> shrinkFactorsPerLevel.SetSize( 1 );
> shrinkFactorsPerLevel[0] = 1;
>
> RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
> smoothingSigmasPerLevel.SetSize( 1 );
> smoothingSigmasPerLevel[0] = 0;
>
> registration->SetNumberOfLevels ( numberOfLevels );
> registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );
> registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );
>
> // Software Guide : BeginLatex
> //
> // Instead of using the whole virtual domain (usually fixed image domain)
> for the registration,
> // we can use a spatial sampled point set by supplying an arbitrary point
> list over which to
> // evaluate the metric. The point list is expected to be in the
> \emph{fixed} image domain, and
> // the points are transformed into the \emph{virtual} domain internally as
> needed. The user can
> // define the point set via \code{SetFixedSampledPointSet()}, and the
> point set is used
> // by calling \code{SetUsedFixedSampledPointSet()}.
> //
> // Also, instead of dealing with the metric directly, the user may define
> // the sampling percentage and sampling strategy for the registration
> framework at each level.
> // In this case, the registration filter manages the sampling operation
> over the fixed image space
> // based on the input strategy (REGULAR, RANDOM) and passes the sampled
> point set to the metric
> // internally.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> RegistrationType::MetricSamplingStrategyType samplingStrategy =
> RegistrationType::RANDOM;
> // Software Guide : EndCodeSnippet
>
> // Software Guide : BeginLatex
> //
> // The number of spatial samples to be
> // used depends on the content of the image. If the images are smooth and
> do
> // not contain many details, the number of spatial samples can usually be
> as low as $1\%$
> // of the total number of pixels in the fixed image. On the other hand, if
> the images are
> // detailed, it may be necessary to use a much higher proportion, such as
> $20\%$ to $50\%$.
> // Increasing the number of samples improves the smoothness of the metric,
> // and therefore helps when this metric is used in conjunction with
> // optimizers that rely of the continuity of the metric values. The
> trade-off, of
> // course, is that a larger number of samples results in longer
> computation
> // times per every evaluation of the metric.
> //
> // One mechanism for bringing the metric to its limit is to disable the
> // sampling and use all the pixels present in the FixedImageRegion. This
> can
> // be done with the \code{SetUseFixedSampledPointSet( false )} method.
> // You may want to try this
> // option only while you are fine tuning all other parameters of your
> // registration. We don't use this method in this current example though.
> //
> // It has been demonstrated empirically that the number of samples is not
> a
> // critical parameter for the registration process. When you start fine
> // tuning your own registration process, you should start using high
> values
> // of number of samples, for example in the range of $20\%$ to $50\%$ of
> the
> // number of pixels in the fixed image. Once you have succeeded to
> register
> // your images you can then reduce the number of samples progressively
> until
> // you find a good compromise on the time it takes to compute one
> evaluation
> // of the metric. Note that it is not useful to have very fast evaluations
> // of the metric if the noise in their values results in more iterations
> // being required by the optimizer to converge. You must then study the
> // behavior of the metric values as the iterations progress, just as
> // illustrated in section~\ref{sec:MonitoringImageRegistration}.
> //
> // \index{itk::Mutual\-Information\-Image\-To\-Image\-Metricv4!Trade-offs}
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> double samplingPercentage = 0.20;
> // Software Guide : EndCodeSnippet
>
> // Software Guide : BeginLatex
> //
> // In ITKv4, a single virtual domain or spatial sample point set is used
> for the
> // all iterations of the registration process. The use of a single sample
> set results
> // in a smooth cost function that can improve the functionality of the
> optimizer.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginCodeSnippet
> registration->SetMetricSamplingStrategy( samplingStrategy );
> registration->SetMetricSamplingPercentage( samplingPercentage );
> // Software Guide : EndCodeSnippet
>
> try
> {
> registration->Update();
> std::cout << "Optimizer stop condition: "
> << registration->GetOptimizer()->GetStopConditionDescription()
> << std::endl;
> }
> catch( itk::ExceptionObject & err )
> {
> std::cerr << "ExceptionObject caught !" << std::endl;
> std::cerr << err << std::endl;
> return EXIT_FAILURE;
> }
>
> TransformType::ParametersType finalParameters =
>
> registration->GetOutput()->Get()->GetParameters();
>
> double TranslationAlongX = finalParameters[0];
> double TranslationAlongY = finalParameters[1];
>
> // For stability reasons it may be desirable to round up the values of
> translation
> //
> unsigned int numberOfIterations = optimizer->GetCurrentIteration();
>
> double bestValue = optimizer->GetValue();
>
>
> // Print out results
> //
> std::cout << std::endl;
> std::cout << "Result = " << std::endl;
> std::cout << " Translation X = " << TranslationAlongX << std::endl;
> std::cout << " Translation Y = " << TranslationAlongY << std::endl;
> std::cout << " Iterations = " << numberOfIterations << std::endl;
> std::cout << " Metric value = " << bestValue << std::endl;
> std::cout << " Stop Condition = " <<
> optimizer->GetStopConditionDescription() << std::endl;
>
> // Software Guide : BeginLatex
> //
> // Let's execute this example over two of the images provided in
> // \code{Examples/Data}:
> //
> // \begin{itemize}
> // \item \code{BrainT1SliceBorder20.png}
> // \item \code{BrainProtonDensitySliceShifted13x17y.png}
> // \end{itemize}
> //
> // \begin{figure}
> // \center
> // \includegraphics[width=0.44\textwidth]{BrainT1SliceBorder20}
> //
> \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceShifted13x17y}
> // \itkcaption[Multi-Modality Registration Inputs]{A T1 MRI (fixed image)
> and a proton
> // density MRI (moving image) are provided as input to the registration
> method.}
> // \label{fig:FixedMovingImageRegistration2}
> // \end{figure}
> //
> // The second image is the result of intentionally translating the image
> // \code{Brain\-Proton\-Density\-Slice\-Border20.png} by $(13,17)$
> // millimeters. Both images have unit-spacing and are shown in Figure
> // \ref{fig:FixedMovingImageRegistration2}. The registration process
> // converges after $46$ iterations and produces the following results:
> //
> // \begin{verbatim}
> // Translation X = 13.0204
> // Translation Y = 17.0006
> // \end{verbatim}
> //
> // These values are a very close match to the true misalignment
> introduced in
> // the moving image.
> //
> // Software Guide : EndLatex
>
> typedef itk::ResampleImageFilter<
> MovingImageType,
> FixedImageType > ResampleFilterType;
>
> ResampleFilterType::Pointer resample = ResampleFilterType::New();
>
> resample->SetTransform( registration->GetTransform() );
> resample->SetInput( movingImageReader->GetOutput() );
>
> FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
>
> PixelType defaultPixelValue = 100;
>
> if( argc > 4 )
> {
> defaultPixelValue = atoi( argv[4] );
> }
>
> resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
> resample->SetOutputOrigin( fixedImage->GetOrigin() );
> resample->SetOutputSpacing( fixedImage->GetSpacing() );
> resample->SetOutputDirection( fixedImage->GetDirection() );
> resample->SetDefaultPixelValue( defaultPixelValue );
>
>
> typedef unsigned char OutputPixelType;
>
> typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
>
> typedef itk::CastImageFilter<
> FixedImageType,
> OutputImageType > CastFilterType;
>
> typedef itk::ImageFileWriter< OutputImageType > WriterType;
>
> WriterType::Pointer writer = WriterType::New();
> CastFilterType::Pointer caster = CastFilterType::New();
>
> writer->SetFileName( argv[3] );
>
> caster->SetInput( resample->GetOutput() );
> writer->SetInput( caster->GetOutput() );
> writer->Update();
>
>
> // Software Guide : BeginLatex
> //
> // \begin{figure}
> // \center
> // \includegraphics[width=0.32\textwidth]{ImageRegistration4Output}
> //
> \includegraphics[width=0.32\textwidth]{ImageRegistration4CheckerboardBefore}
> //
> \includegraphics[width=0.32\textwidth]{ImageRegistration4CheckerboardAfter}
> // \itkcaption[MattesMutualInformationImageToImageMetricv4 output
> images]{The mapped
> // moving image (left) and the composition of fixed and moving images
> before
> // (center) and after (right) registration with Mattes mutual
> information.}
> // \label{fig:ImageRegistration4Output}
> // \end{figure}
> //
> // The result of resampling the moving image is presented on the left of
> // Figure \ref{fig:ImageRegistration4Output}. The center and right parts
> of
> // the figure present a checkerboard composite of the fixed and moving
> // images before and after registration respectively.
> //
> // Software Guide : EndLatex
>
>
> //
> // Generate checkerboards before and after registration
> //
> typedef itk::CheckerBoardImageFilter< FixedImageType >
> CheckerBoardFilterType;
>
> CheckerBoardFilterType::Pointer checker = CheckerBoardFilterType::New();
>
> checker->SetInput1( fixedImage );
> checker->SetInput2( resample->GetOutput() );
>
> caster->SetInput( checker->GetOutput() );
> writer->SetInput( caster->GetOutput() );
>
> resample->SetDefaultPixelValue( 0 );
>
> // Before registration
> TransformType::Pointer identityTransform = TransformType::New();
> identityTransform->SetIdentity();
> resample->SetTransform( identityTransform );
>
> if( argc > 5 )
> {
> writer->SetFileName( argv[5] );
> writer->Update();
> }
>
>
> // After registration
> resample->SetTransform( registration->GetTransform() );
> if( argc > 6 )
> {
> writer->SetFileName( argv[6] );
> writer->Update();
> }
>
>
> // Software Guide : BeginLatex
> //
> // \begin{figure}
> // \center
> //
> \includegraphics[width=0.44\textwidth]{ImageRegistration4TraceTranslations}
> //
> \includegraphics[width=0.44\textwidth]{ImageRegistration4TraceTranslations2}
> //
> \includegraphics[width=0.6\textwidth,height=5cm]{ImageRegistration4TraceMetric}
> // \itkcaption[MattesMutualInformationImageToImageMetricv4 output
> plots]{Sequence
> // of translations and metric values at each iteration of the optimizer.}
> // \label{fig:ImageRegistration4TraceTranslations}
> // \end{figure}
> //
> // Figure \ref{fig:ImageRegistration4TraceTranslations} (upper-left)
> shows
> // the sequence of translations followed by the optimizer as it searched
> the
> // parameter space. The upper-right figure presents a closer look at the
> // convergence basin for the last iterations of the optimizer. The bottom
> of
> // the same figure shows the sequence of metric values computed as the
> // optimizer searched the parameter space.
> //
> // Software Guide : EndLatex
>
> // Software Guide : BeginLatex
> //
> // You must note however that there are a number of non-trivial issues
> // involved in the fine tuning of parameters for the optimization. For
> // example, the number of bins used in the estimation of Mutual
> Information
> // has a dramatic effect on the performance of the optimizer. In order to
> // illustrate this effect, the same example has been executed using a
> range
> // of different values for the number of bins, from $10$ to $30$. If you
> // repeat this experiment, you will notice that depending on the number of
> // bins used, the optimizer's path may get trapped early on in local
> minima.
> // Figure \ref{fig:ImageRegistration4TraceTranslationsNumberOfBins} shows
> the
> // multiple paths that the optimizer took in the parametric space of the
> // transform as a result of different selections on the number of bins
> used
> // by the Mattes Mutual Information metric. Note that many of the paths
> die
> // in local minima instead of reaching the extrema value on the upper
> right
> // corner.
> //
> // \begin{figure}
> // \center
> //
> \includegraphics[width=0.8\textwidth]{ImageRegistration4TraceTranslationsNumberOfBins}
> // \itkcaption[MattesMutualInformationImageToImageMetricv4 number of
> // bins]{Sensitivity of the optimization path to the number of Bins used
> for
> // estimating the value of Mutual Information with Mattes et al.
> approach.}
> // \label{fig:ImageRegistration4TraceTranslationsNumberOfBins}
> // \end{figure}
> //
> //
> // Effects such as the one illustrated here highlight how useless is to
> // compare different algorithms based on a non-exhaustive search of their
> // parameter setting. It is quite difficult to be able to claim that a
> // particular selection of parameters represent the best combination for
> // running a particular algorithm. Therefore, when comparing the
> performance
> // of two or more different algorithms, we are faced with the challenge of
> // proving that none of the algorithms involved in the comparison are
> being
> // run with a sub-optimal set of parameters.
> //
> // Software Guide : EndLatex
>
>
> // Software Guide : BeginLatex
> //
> // The plots in Figures~\ref{fig:ImageRegistration4TraceTranslations}
> // and~\ref{fig:ImageRegistration4TraceTranslationsNumberOfBins} were
> // generated using Gnuplot\footnote{\url{http://www.gnuplot.info/}}.
> // The scripts used for this purpose are available
> // in the \code{ITKSoftwareGuide} Git repository under the directory
> //
> // ~\code{ITKSoftwareGuide/SoftwareGuide/Art}.
> //
> // Data for the plots were taken directly from the output that the
> // Command/Observer in this example prints out to the console. The output
> // was processed with the UNIX editor
> // \code{sed}\footnote{\url{http://www.gnu.org/software/sed/sed.html}} in
> // order to remove commas and brackets that were confusing for Gnuplot's
> // parser. Both the shell script for running \code{sed} and for running
> // {Gnuplot} are available in the directory indicated above. You may find
> // useful to run them in order to verify the results presented here, and
> to
> // eventually modify them for profiling your own registrations.
> //
> // \index{Open Science}
> //
> // Open Science is not just an abstract concept. Open Science is
> something
> // to be practiced every day with the simple gesture of sharing
> information
> // with your peers, and by providing all the tools that they need for
> // replicating the results that you are reporting. In Open Science, the
> only
> // bad results are those that can not be
> // replicated\footnote{\url{http://science.creativecommons.org/}}.
> Science
> // is dead when people blindly trust authorities~\footnote{For example:
> // Reviewers of Scientific Journals.} instead of verifying their
> statements
> // by performing their own experiments ~\cite{Popper1971,Popper2002}.
> //
> // Software Guide : EndLatex
>
>
> return EXIT_SUCCESS;
> }
>
>
> <http://itk-users.7.n7.nabble.com/file/n37612/0000.png>
>
>
>
>
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