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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.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// This example illustrates how to do registration with a 2D Rigid Transform
// and with MutualInformation metric.
//
// Software Guide : EndLatex
#include "itkImageRegistrationMethodv4.h"
#include "itkCenteredRigid2DTransform.h"
#include "itkCenteredTransformInitializer.h"
// Software Guide : BeginCodeSnippet
#include "itkMattesMutualInformationImageToImageMetricv4.h"
// Software Guide : EndCodeSnippet
#include "itkRegularStepGradientDescentOptimizerv4.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.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 < 3 )
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile ";
std::cerr << "outputImagefile " << 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;
// Software Guide : BeginLatex
//
// The CenteredRigid2DTransform applies a rigid transform in 2D space.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::CenteredRigid2DTransform< double > TransformType;
// Software Guide : EndCodeSnippet
typedef itk::RegularStepGradientDescentOptimizerv4<double> OptimizerType;
typedef itk::ImageRegistrationMethodv4<
FixedImageType,
MovingImageType,
TransformType > RegistrationType;
// Software Guide : BeginCodeSnippet
typedef itk::MattesMutualInformationImageToImageMetricv4<
FixedImageType,
MovingImageType > MetricType;
// Software Guide : EndCodeSnippet
TransformType::Pointer transform = TransformType::New();
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetOptimizer( optimizer );
registration->SetMetric( metric );
// For consistent results when regression testing.
registration->MetricSamplingReinitializeSeed(121212);
// Software Guide : BeginCodeSnippet
metric->SetNumberOfHistogramBins( 20 );
double samplingPercentage = 0.20;
registration->SetMetricSamplingPercentage( samplingPercentage );
RegistrationType::MetricSamplingStrategyType samplingStrategy =
RegistrationType::RANDOM;
registration->SetMetricSamplingStrategy( samplingStrategy );
// 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() );
fixedImageReader->Update();
// Software Guide : BeginLatex
//
// The \doxygen{CenteredRigid2DTransform} is initialized with 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, the first of which 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
typedef itk::CenteredTransformInitializer<
TransformType,
FixedImageType,
MovingImageType > TransformInitializerType;
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->SetInitialTransform( transform );
registration->InPlaceOn();
// Software Guide : BeginLatex
//
// The optimizer scales the metrics (the gradient in this case) by the
// scales during each iteration. Therefore, 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
typedef OptimizerType::ScalesType OptimizerScalesType;
OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
const double translationScale = 1.0 / 128.0;
const 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->SetLearningRate( 0.5 );
optimizer->SetMinimumStepLength( 0.0001 );
optimizer->SetNumberOfIterations( 400 );
// Software Guide : EndCodeSnippet
// 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 );
// 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;
}
typedef TransformType::ParametersType ParametersType;
ParametersType finalParameters = transform->GetParameters();
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;
typedef itk::ResampleImageFilter<
MovingImageType,
FixedImageType > ResampleFilterType;
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform( transform );
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 );
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();
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{BrainProtonDensitySliceR10X13Y17.png}
// \end{itemize}
//
// The second image is the result of intentionally rotating the first
// image by $10$ degrees and shifting it $13mm$ in $X$ and $17mm$ in
// $Y$. Both images have unit-spacing and are shown in Figure
// \ref{fig:FixedMovingImageRegistration5}. The example
// yielded the following results.
//
// \begin{verbatim}
//
// Angle (radians) 0.174585
// Angle (degrees) 10.003
// Center X = 110
// Center Y = 128
// Translation X = 13.09
// Translation Y = 15.91
//
// \end{verbatim}
//
// These values match the true misalignment introduced in the moving image.
//
// Software Guide : EndLatex
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