ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/ImageRegistration13.cxx
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*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
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*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates how to do registration with a 2D Rigid Transform
// and with MutualInformation metric.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// 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:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro( Self );
protected:
CommandIterationUpdate() {};
public:
using OptimizerPointer = const OptimizerType *;
void Execute(itk::Object *caller, const itk::EventObject & event) override
{
Execute( (const itk::Object *)caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event) 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 < 3 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile ";
std::cerr << "outputImagefile ";
std::cerr << "[useExplicitPDFderivatives ] ";
std::cerr << "[useCachingBSplineWeights ] " << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = unsigned char;
using FixedImageType = itk::Image< PixelType, Dimension >;
using MovingImageType = itk::Image< PixelType, Dimension >;
// Software Guide : BeginLatex
// The CenteredRigid2DTransform applies a rigid transform in 2D space.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
using InterpolatorType = itk::LinearInterpolateImageFunction<
MovingImageType,
double >;
using RegistrationType = itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType >;
// Software Guide : BeginCodeSnippet
FixedImageType,
MovingImageType >;
// Software Guide : EndCodeSnippet
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
// Software Guide : EndCodeSnippet
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetOptimizer( optimizer );
registration->SetTransform( transform );
registration->SetInterpolator( interpolator );
MetricType::Pointer metric = MetricType::New();
registration->SetMetric( metric );
metric->SetNumberOfHistogramBins( 20 );
metric->SetNumberOfSpatialSamples( 10000 );
// For consistent results when regression testing.
metric->ReinitializeSeed( 121212 );
if( argc > 4 )
{
// Define whether to calculate the metric derivative by explicitly
// computing the derivatives of the joint PDF with respect to the Transform
// parameters, or doing it by progressively accumulating contributions from
// each bin in the joint PDF.
metric->SetUseExplicitPDFDerivatives( std::stoi( argv[4] ) );
}
if( argc > 5 )
{
// Define whether to cache the BSpline weights and indexes corresponding to
// each one of the samples used to compute the metric. Enabling caching will
// make the algorithm run faster but it will have a cost on the amount of memory
// that needs to be allocated. This option is only relevant when using the
// BSplineTransform.
metric->SetUseCachingOfBSplineWeights( std::stoi( argv[5] ) );
}
using FixedImageReaderType = itk::ImageFileReader< FixedImageType >;
using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;
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() );
fixedImageReader->Update();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion() );
// Software Guide : BeginLatex
// The \doxygen{CenteredRigid2DTransform} is initialized by 5 parameters,
// indicating the angle of rotation, the center coordinates and the
// translation to be applied after rotation. The initialization is done
// by the \doxygen{CenteredTransformInitializer}.
// The transform can operate in two modes, one assumes that the
// anatomical objects to be registered are centered in their respective
// images. Hence the best initial guess for the registration is the one
// that superimposes those two centers.
// This second approach assumes that the moments of the anatomical
// objects are similar for both images and hence the best initial guess
// for registration is to superimpose both mass centers. The center of
// mass is computed from the moments obtained from the gray level values.
// Here we adopt the first approach. The \code{GeometryOn()} method
// toggles between the approaches.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformInitializerType = itk::CenteredTransformInitializer<
TransformType, FixedImageType,
MovingImageType >;
TransformInitializerType::Pointer initializer
= TransformInitializerType::New();
initializer->SetTransform( transform );
initializer->SetFixedImage( fixedImageReader->GetOutput() );
initializer->SetMovingImage( movingImageReader->GetOutput() );
initializer->GeometryOn();
initializer->InitializeTransform();
// Software Guide : EndCodeSnippet
transform->SetAngle( 0.0 );
registration->SetInitialTransformParameters( transform->GetParameters() );
// Software Guide : BeginLatex
// The optimizer scales the metrics (the gradient in this case) by the
// scales during each iteration. Hence a large value of the center scale
// will prevent movement along the center during optimization. Here we
// assume that the fixed and moving images are likely to be related by
// a translation.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
const double translationScale = 1.0 / 128.0;
constexpr double centerScale = 1000.0; // prevents it from moving
// during the optimization
optimizerScales[0] = 1.0;
optimizerScales[1] = centerScale;
optimizerScales[2] = centerScale;
optimizerScales[3] = translationScale;
optimizerScales[4] = translationScale;
optimizer->SetScales( optimizerScales );
optimizer->SetMaximumStepLength( 0.5 );
optimizer->SetMinimumStepLength( 0.0001 );
optimizer->SetNumberOfIterations( 400 );
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver( itk::IterationEvent(), observer );
try
{
registration->Update();
std::cout << "Optimizer stop condition = "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch( itk::ExceptionObject & err )
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
using ParametersType = RegistrationType::ParametersType;
ParametersType finalParameters = registration->GetLastTransformParameters();
const double finalAngle = finalParameters[0];
const double finalRotationCenterX = finalParameters[1];
const double finalRotationCenterY = finalParameters[2];
const double finalTranslationX = finalParameters[3];
const double finalTranslationY = finalParameters[4];
unsigned int numberOfIterations = optimizer->GetCurrentIteration();
double bestValue = optimizer->GetValue();
// Print out results
//
const double finalAngleInDegrees = finalAngle * 180 / itk::Math::pi;
std::cout << "Result = " << std::endl;
std::cout << " Angle (radians) " << finalAngle << std::endl;
std::cout << " Angle (degrees) " << finalAngleInDegrees << std::endl;
std::cout << " Center X = " << finalRotationCenterX << std::endl;
std::cout << " Center Y = " << finalRotationCenterY << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
using ResampleFilterType = itk::ResampleImageFilter<
MovingImageType,
FixedImageType >;
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters( finalParameters );
finalTransform->SetFixedParameters( transform->GetFixedParameters() );
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( finalTransform );
resample->SetInput( movingImageReader->GetOutput() );
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resample->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
resample->SetOutputOrigin( fixedImage->GetOrigin() );
resample->SetOutputSpacing( fixedImage->GetSpacing() );
resample->SetOutputDirection( fixedImage->GetDirection() );
resample->SetDefaultPixelValue( 100 );
using OutputImageType = itk::Image< PixelType, Dimension >;
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[3] );
writer->SetInput( resample->GetOutput() );
writer->Update();
return EXIT_SUCCESS;
}
// Software Guide : BeginLatex
//
// Let's execute this example over some of the images provided in
// \code{Examples/Data}, for example:
//
// \begin{itemize}
// \item \code{BrainProtonDensitySlice.png}
// \item \code{BrainProtonDensitySliceBorder20.png}
// \end{itemize}
//
// The second image is the result of intentionally shifting the first
// image by $20mm$ in $X$ and $20mm$ in
// $Y$. Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration1}. The example
// yielded the following results.
//
// \begin{verbatim}
// Translation X = 20
// Translation Y = 20
// \end{verbatim}
// These values match the true misalignment introduced in the moving image.
// Software Guide : EndLatex