ITK  4.6.0
Insight Segmentation and Registration Toolkit
RegistrationITKv4/ImageRegistration11.cxx
/*=========================================================================
*
* 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 combine the MutualInformation metric with an
// Evolutionary algorithm for optimization. Evolutionary algorithms are
// naturally well-suited for optimizing the Mutual Information metric given its
// random and noisy behavior.
//
// The structure of the example is almost identical to the one illustrated in
// ImageRegistration4. Therefore we focus here on the setup that is
// specifically required for the evolutionary optimizer.
//
//
// \index{itk::ImageRegistrationMethod!Multi-Modality}
// \index{itk::OnePlusOneEvolutionaryOptimizer!Multi-Modality}
//
// 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:
typedef CommandIterationUpdate Self;
itkNewMacro( Self );
protected:
CommandIterationUpdate() { m_LastMetricValue = 0.0; };
public:
typedef itk::OnePlusOneEvolutionaryOptimizer 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;
}
double currentValue = optimizer->GetValue();
// Only print out when the Metric value changes
if( std::fabs( m_LastMetricValue - currentValue ) > 1e-7 )
{
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << currentValue << " ";
std::cout << optimizer->GetCurrentPosition() << std::endl;
m_LastMetricValue = currentValue;
}
}
private:
double m_LastMetricValue;
};
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::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 2;
typedef unsigned short PixelType;
typedef itk::Image< PixelType, Dimension > FixedImageType;
typedef itk::Image< PixelType, Dimension > MovingImageType;
typedef itk::OnePlusOneEvolutionaryOptimizer OptimizerType;
MovingImageType,
double > InterpolatorType;
FixedImageType,
MovingImageType > 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
FixedImageType,
MovingImageType > MetricType;
// Software Guide : EndCodeSnippet
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
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 );
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( atoi( argv[4] ) );
}
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();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion() );
typedef RegistrationType::ParametersType ParametersType;
ParametersType initialParameters( transform->GetNumberOfParameters() );
initialParameters[0] = 0.0; // Initial offset in mm along X
initialParameters[1] = 0.0; // Initial offset in mm along Y
registration->SetInitialTransformParameters( initialParameters );
// Software Guide : BeginLatex
//
// Evolutionary algorithms are based on testing random variations
// of parameters. In order to support the computation of random values,
// ITK provides a family of random number generators. In this example, we
// use the \doxygen{NormalVariateGenerator} which generates values with a
// normal distribution.
//
// \index{itk::NormalVariateGenerator!New()}
// \index{itk::NormalVariateGenerator!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
GeneratorType::Pointer generator = GeneratorType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The random number generator must be initialized with a seed.
//
// \index{itk::NormalVariateGenerator!Initialize()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
generator->Initialize(12345);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Another significant difference in the metric is that it
// computes the negative mutual information and hence we
// need to minimize the cost function in this case. In this
// example we will use the same optimization parameters as in
// Section \ref{sec:IntroductionImageRegistration}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->MaximizeOff();
optimizer->SetNormalVariateGenerator( generator );
optimizer->Initialize( 10 );
optimizer->SetEpsilon( 1.0 );
optimizer->SetMaximumIteration( 4000 );
// 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 << "Registration completed!" << std::endl;
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;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[0];
double TranslationAlongY = finalParameters[1];
unsigned int numberOfIterations = optimizer->GetCurrentIteration();
double bestValue = optimizer->GetValue();
// Print out results
//
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;
// Software Guide : BeginLatex
//
// This example is executed using the same multi-modality images as
// in the previous one. The registration converges after $24$ iterations and produces
// the following results:
//
// \begin{verbatim}
// Translation X = 13.1719
// Translation Y = 16.9006
// \end{verbatim}
// These values are a very close match to
// the true misalignment introduced in the moving image.
//
// Software Guide : EndLatex
MovingImageType,
FixedImageType > ResampleFilterType;
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 );
typedef unsigned char OutputPixelType;
FixedImageType,
OutputImageType > CastFilterType;
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;
}