[ITK] [ITK-users] SimpleITK for Java Image Fusion and Co-registration?
ivan
ivan.granata.na at gmail.com
Thu Sep 29 09:30:18 EDT 2016
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;
}
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