ITK  5.0.0
Insight Segmentation and Registration Toolkit
Examples/RegistrationITKv3/ImageRegistration16.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 do registration with a 2D Translation Transform,
// the Normalized Mutual Information metric and the Amoeba optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// 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()
{
m_IterationNumber=0;
}
public:
using OptimizerType = itk::AmoebaOptimizer;
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 << m_IterationNumber++ << " ";
std::cout << optimizer->GetCachedValue() << " ";
std::cout << optimizer->GetCachedCurrentPosition() << std::endl;
}
private:
unsigned long m_IterationNumber;
};
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 ";
std::cerr << " [initialTx] [initialTy]";
std::cerr << "[useExplicitPDFderivatives ] " << 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 >;
using OptimizerType = itk::AmoebaOptimizer;
using InterpolatorType = itk::LinearInterpolateImageFunction<
MovingImageType,
double >;
using RegistrationType = itk::ImageRegistrationMethod<
FixedImageType,
MovingImageType >;
FixedImageType,
MovingImageType >;
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 );
// Software Guide : BeginLatex
//
// The metric requires two parameters to be selected: the number
// of bins used to compute the entropy and the number of spatial samples
// used to compute the density estimates. In typical application, 50
// histogram bins are sufficient and the metric is relatively insensitive
// to changes in the number of bins. The number of spatial samples
// to be used depends on the content of the image. If the images are
// smooth and do not contain much detail, then using approximately
// $1$ percent of the pixels will do. On the other hand, if the images
// are detailed, it may be necessary to use a much higher proportion,
// such as $20$ percent.
//
// \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfHistogramBins()}
// \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfSpatialSamples()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->SetNumberOfHistogramBins( 20 );
metric->SetNumberOfSpatialSamples( 10000 );
// Software Guide : EndCodeSnippet
// For consistent results when regression testing.
metric->ReinitializeSeed( 121213 );
if( argc > 6 )
{
// 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[6] ) );
}
const unsigned int numberOfParameters = transform->GetNumberOfParameters();
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();
movingImageReader->Update();
FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
registration->SetFixedImageRegion( fixedImage->GetBufferedRegion() );
transform->SetIdentity();
using ParametersType = RegistrationType::ParametersType;
ParametersType initialParameters = transform->GetParameters();
initialParameters[0] = 0.0;
initialParameters[1] = 0.0;
if( argc > 5 )
{
initialParameters[0] = std::stod( argv[4] );
initialParameters[1] = std::stod( argv[5] );
}
registration->SetInitialTransformParameters( initialParameters );
std::cout << "Initial transform parameters = ";
std::cout << initialParameters << std::endl;
// Software Guide : BeginLatex
//
// The AmoebaOptimizer moves a simplex around the cost surface. Here we set
// the initial size of the simplex (5 units in each of the parameters)
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerType::ParametersType simplexDelta( numberOfParameters );
simplexDelta.Fill( 5.0 );
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 0.1 (which will be one
// tenth 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 value of Mutual Information. So we set the function
// convergence to be 0.001 bits (bits are the appropriate units for
// measuring Information).
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetParametersConvergenceTolerance( 0.1 ); // 1/10th pixel
optimizer->SetFunctionConvergenceTolerance(0.001); // 0.001 bits
// 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 );
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;
}
ParametersType finalParameters = registration->GetLastTransformParameters();
const double finalTranslationX = finalParameters[0];
const double finalTranslationY = finalParameters[1];
double bestValue = optimizer->GetValue();
// Print out results
//
std::cout << "Result = " << std::endl;
std::cout << " Translation X = " << finalTranslationX << std::endl;
std::cout << " Translation Y = " << finalTranslationY << 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() );
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();
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}