[Insight-users] Affine registration labels
Roger Alvaredo
roger.alvaredo at gmail.com
Wed Dec 14 10:50:04 EST 2005
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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile:ImageRegistration9.cxx,v $
Language: C++
Date: $Date: 2005/08/31 14:14:15 $
Version: $Revision: 1.13 $
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
// Software Guide : BeginLatex
//
// This example illustrates the use of the image registration framework in
// Insight to align two label maps. Common structures are assumed to
// use the same label. The registration metric simply counts the
// number of corresponding pixels that have the same label.
//
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImageRegistrationMethod.h"
#include "itkAffineTransform.h"
#include "itkMatchCardinalityImageToImageMetric.h"
#include "itkNearestNeighborInterpolateImageFunction.h"
#include "itkAmoebaOptimizer.h"
// Software Guide : EndCodeSnippet
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkFileOutputWindow.h"
//
// The following piece of code implements an observer
// that will 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::AmoebaOptimizer OptimizerType;
typedef const OptimizerType * OptimizerPointer;
void Execute(itk::Object *caller, const itk::EventObject & event)
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event)
{
OptimizerPointer optimizer =
dynamic_cast< OptimizerPointer >( object );
if( ! itk::IterationEvent().CheckEvent( &event ) )
{
return;
}
// std::cout << optimizer->GetCachedValue() << " ";
// std::cout << optimizer->GetCachedCurrentPosition() << std::endl;
}
};
int main( int argc, char *argv[] )
{
if( argc < 3 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile ";
std::cerr << " outputImagefile" << std::endl;
return 1;
}
itk::FileOutputWindow::Pointer fow = itk::FileOutputWindow::New();
fow->SetInstance( fow );
// The types of each one of the components in the registration methods should
// be instantiated. First, we select the image dimension and the type for
// representing image pixels.
//
const unsigned int Dimension = 3;
typedef float PixelType;
// The types of the input images are instantiated by the following lines.
//
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
// Software Guide : BeginLatex
// The transform that will map one image space into the other is defined
// below.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::AffineTransform< double, Dimension > TransformType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// An optimizer is required to explore the parameter space of the transform
// in search of optimal values of the metric. The metric selected
// does not require analytical derivatives of its cost function.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::AmoebaOptimizer OptimizerType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// The metric will compare how well the two images match each
// other. Metric types are usually parametrized by the image types
// as can be seen in the following type declaration. The metric
// selected here is suitable for comparing two label maps where the
// labels are consistent between the two maps. This metric
// measures the percentage of pixels that exactly match or
// mismatch.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::MatchCardinalityImageToImageMetric<
FixedImageType,
MovingImageType > MetricType;
// Software Guide : EndCodeSnippet
// Finally, the type of the interpolator is declared. The
// interpolator will evaluate the moving image at non-grid
// positions.
// Software Guide : BeginLatex
// Since we are registering label maps, we use a
// NearestNeighborInterpolateImageFunction to ensure subpixel
// values are not interpolated (to labels that do not exist).
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk:: NearestNeighborInterpolateImageFunction<
MovingImageType,
double > InterpolatorType;
// Software Guide : EndCodeSnippet
// The registration method type is instantiated using the types of the
// fixed and moving images. This class is responsible for interconnecting
// all the components we have described so far.
typedef itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType > RegistrationType;
// Each one of the registration components is created using its
// \code{New()} method and is assigned to its respective
// \doxygen{SmartPointer}.
//
// Software Guide : BeginCodeSnippet
MetricType::Pointer metric = MetricType::New();
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// We are using a MatchCardinalityImageToImageMetric to compare two
// label maps. This metric simple counts the percentage of
// corresponding pixels that have the same label. This metric does
// not provide analytical derivatives, so we will use an
// AmoebaOptimizer to drive the registration. The AmoebaOptimizer
// can only minimize a cost function, so we set the metric to count
// the percentages of mismatches.
// Software Guide : BeginLatex
// Software Guide : BeginCodeSnippet
metric->MeasureMatchesOff();
// Software Guide : EndCodeSnippet
// Each component is now connected to the instance of the registration method.
// \index{itk::RegistrationMethod!SetMetric()}
// \index{itk::RegistrationMethod!SetOptimizer()}
// \index{itk::RegistrationMethod!SetTransform()}
// \index{itk::RegistrationMethod!SetFixedImage()}
// \index{itk::RegistrationMethod!SetMovingImage()}
// \index{itk::RegistrationMethod!SetInterpolator()}
//
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] );
// In this example, the fixed and moving images are read from files. This
// requires the \doxygen{ImageRegistrationMethod} to acquire its inputs to
// the output of the readers.
//
registration->SetFixedImage( fixedImageReader->GetOutput() );
registration->SetMovingImage( movingImageReader->GetOutput() );
// The registration can be restricted to consider only a particular region
// of the fixed image as input to the metric computation. This region is
// defined by the \code{SetFixedImageRegion()} method. You could use this
// feature to reduce the computational time of the registration or to avoid
// unwanted objects present in the image affecting the registration outcome.
// In this example we use the full available content of the image. This
// region is identified by the \code{BufferedRegion} of the fixed image.
// Note that for this region to be valid the reader must first invoke its
// \code{Update()} method.
//
// \index{itk::ImageRegistrationMethod!SetFixedImageRegion()}
// \index{itk::Image!GetBufferedRegion()}
//
fixedImageReader->Update();
movingImageReader->Update();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion() );
// The parameters of the transform are initialized by passing them in an
// array. This can be used to setup an initial known correction of the
// misalignment. In this particular case, a translation transform is
// being used for the registration. The array of parameters for this
// transform is simply composed of the translation values along each
// dimension. Setting the values of the parameters to zero
// initializes the transform as an \emph{identity} transform. Note that the
// array constructor requires the number of elements as an argument.
//
// \index{itk::TranslationTransform!GetNumberOfParameters()}
// \index{itk::RegistrationMethod!SetInitialTransformParameters()}
//
/**********************************/
// Display initial transform paramters
std::cout << "Initial Transform Parameters: " << std::endl;
std::cout << transform->GetParameters() << std::endl;
/************************************/
double translationScale = 1.0 / 1.000;
std::cout << "begin optimizer scales" << std::endl;
typedef OptimizerType::ScalesType OptimizerScalesType;
OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
optimizerScales[0] = 1.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = 1.0;
optimizerScales[3] = 1.0;
optimizerScales[4] = 1.0;
optimizerScales[5] = 1.0;
optimizerScales[6] = 1.0;
optimizerScales[7] = 1.0;
optimizerScales[8] = 1.0;
optimizerScales[9] = translationScale;
optimizerScales[10] = translationScale;
optimizerScales[11] = translationScale;
optimizer->SetScales( optimizerScales );
std::cout << "end optimizer scales" << std::endl;
OptimizerType::ParametersType simplexDelta( transform->GetNumberOfParameters() );
std::cout << transform->GetNumberOfParameters()<< std::endl;
simplexDelta.Fill( 1.0 );
std::cout << simplexDelta << std::endl;
optimizer->AutomaticInitialSimplexOff();
optimizer->SetInitialSimplexDelta( simplexDelta );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// We also adjust the tolerances on the optimizer to define
// convergence. Here, we used a tolerance on the parameters of
// 0.25 (which will be a quarter of image unit, in this case
// pixels). We also set the tolerance on the cost function value to
// define convergence. The metric we are using returns the
// percentage of pixels that mismatch. So we set the function
// convergence to be 0.1%
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetParametersConvergenceTolerance( 0.25 ); // quarter pixel
optimizer->SetFunctionConvergenceTolerance(0.001); // 0.1%
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// In the case where the optimizer never succeeds in reaching the desired
// precision tolerance, it is prudent to establish a limit on the number of
// iterations to be performed. This maximum number is defined with the
// method \code{SetMaximumNumberOfIterations()}.
//
// \index{itk::Amoeba\-Optimizer!SetMaximumNumberOfIterations()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetMaximumNumberOfIterations( 200 );
// Software Guide : EndCodeSnippet
//
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
registration->SetMetric( metric );
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetInterpolator( interpolator );
// The registration process is triggered by an invocation of the
// \code{StartRegistration()} method. If something goes wrong during the
// initialization or execution of the registration an exception will be
// thrown. We should therefore place the \code{StartRegistration()} method
// in a \code{try/catch} block as illustrated in the following lines.
//
try
{
// print out the initial metric value. need to initialize the
// registration method to force all the connections to be established.
std::cout << "Before initialize registration" << std::endl;
registration->Initialize();
// std::cout << "Initial Metric value = " << metric->GetValue( transform->GetParameters() ) << std::endl;
// run the registration
std::cout << "Registration... " << std::endl;
registration->StartRegistration();
}
catch( itk::ExceptionObject & err )
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return -1;
}
// In a real application, you may attempt to recover from the error in the
// catch block. Here we are simply printing out a message and then
// terminating the execution of the program.
//
//
// The result of the registration process is an array of parameters that
// defines the spatial transformation in an unique way. This final result is
// obtained using the \code{GetLastTransformParameters()} method.
//
// \index{itk::RegistrationMethod!GetLastTransformParameters()}
//
OptimizerType::ParametersType finalParameters = registration->GetLastTransformParameters();
const unsigned int numberOfIterations = optimizer->GetOptimizer()->get_num_evaluations();
const double bestValue = metric->GetValue(finalParameters);
const double versorX = finalParameters[0];
const double versorY = finalParameters[1];
const double versorZ = finalParameters[2];
const double finalTranslationX = finalParameters[3];
const double finalTranslationY = finalParameters[4];
const double finalTranslationZ = finalParameters[5];
// Print out results
//
std::cout << std::endl << std::endl;
std::cout << "Result = " << std::endl;
std::cout << " versor X = " << versorX << std::endl;
std::cout << " versor Y = " << versorY << std::endl;
std::cout << " versor Z = " << versorZ << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Translation Z = " << finalTranslationZ << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// It is common, as the last step of a registration task, to use the
// resulting transform to map the moving image into the fixed image space.
// This is easily done with the \doxygen{ResampleImageFilter}. Please
// refer to Section~\ref{sec:ResampleImageFilter} for details on the use
// of this filter. First, a ResampleImageFilter type is instantiated
// using the image types. It is convenient to use the fixed image type as
// the output type since it is likely that the transformed moving image
// will be compared with the fixed image.
//
typedef itk::ResampleImageFilter<
MovingImageType,
FixedImageType > ResampleFilterType;
// A transform of the same type used in the registration process should be
// created and initialized with the parameters resulting from the
// registration process.
//
// \index{itk::ImageRegistrationMethod!Resampling image}
//
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters( finalParameters );
// Then a resampling filter is created and the corresponding transform and
// moving image connected as inputs.
//
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( finalTransform );
resample->SetInput( movingImageReader->GetOutput() );
// As described in Section \ref{sec:ResampleImageFilter}, the
// ResampleImageFilter requires additional parameters to be
// specified, in particular, the spacing, origin and size of the output
// image. The default pixel value is also set to the standard label
// for "unknown" or background. Finally, we need to set the
// interpolator to be the same type of interpolator as the
// registration method used (nearest neighbor).
//
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
//resample->SetDefaultPixelValue( 0 );
resample->SetInterpolator( interpolator );
// The output of the filter is passed to a writer that will store the
// image in a file. An \doxygen{CastImageFilter} is used to convert the
// pixel type of the resampled image to the final type used by the
// writer. The cast and writer filters are instantiated below.
//
typedef float OutputPixelType;
typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
typedef itk::CastImageFilter<
FixedImageType,
OutputImageType > CastFilterType;
typedef itk::ImageFileWriter< OutputImageType > WriterType;
// The filters are created by invoking their \code{New()}
// method.
//
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
writer->SetFileName( argv[3] );
// The \code{Update()} method of the writer is invoked in order to trigger
// the execution of the pipeline.
//
caster->SetInput( resample->GetOutput() );
writer->SetInput( caster->GetOutput() );
std::cout << std::endl <<"Writing image... :" << argv[3] <<std::endl;
writer->Update();
//
// The fixed image and the transformed moving image can easily be compared
// using the \code{SquaredDifferenceImageFilter}. This pixel-wise
// filter computes the squared value of the difference between homologous
// pixels of its input images.
//
return 0;
}
// SoftwareGuide : BeginLatex
// The example was run on two binary images. The first binary image was generated by running the
// confidence connected image filter (section \ref{sec:ConfidenceConnected}) on
// the MRI slice of the brain. The second was generated similarly after
// shifting the slice by 13 pixels horizontally and 17 pixels
// vertically. The Amoeba optimizer converged after 34 iterations
// and produced the following results:
//
// \begin{verbatim}
// Translation X = 12.5
// Translation Y = 16.77
// \end{verbatim}
// These results are a close match to the true misalignment.
// SoftwareGuide : EndLatex
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