ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration11.cxx
/*=========================================================================
*
* Copyright NumFOCUS
*
* 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::ImageRegistrationMethodv4!Multi-Modality}
// \index{itk::OnePlusOneEvolutionaryOptimizerv4!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:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() { m_LastMetricValue = 0.0; };
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
{
auto optimizer = static_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 << "[samplingPercentage ] " << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
using 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
using MetricType =
MovingImageType>;
// Software Guide : EndCodeSnippet
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
MetricType::Pointer metric = MetricType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetOptimizer(optimizer);
registration->SetMetric(metric);
// Software Guide : BeginLatex
//
// The histogram bins metric parameter is set as follows.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->SetNumberOfHistogramBins(20);
// Software Guide : EndCodeSnippet
double samplingPercentage = 0.20;
if (argc > 4)
{
samplingPercentage = std::stod(argv[4]);
}
// Software Guide : BeginLatex
//
// As our previous discussion in section
// ~\ref{sec:MultiModalityRegistrationMattes}, only a subsample of the
// virtual domain is needed to evaluate the metric. The number of spatial
// samples to be used depends on the content of the image, and the user can
// define the sampling percentage and the way that sampling operation is
// managed by the registration framework as follows. Sampling startegy can
// can be defined as \code{REGULAR} or \code{RANDOM}, while the default
// value is \code{NONE}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetMetricSamplingPercentage(samplingPercentage);
RegistrationType::MetricSamplingStrategyEnum samplingStrategy =
RegistrationType::MetricSamplingStrategyEnum::RANDOM;
registration->SetMetricSamplingStrategy(samplingStrategy);
// Software Guide : EndCodeSnippet
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();
using ParametersType = TransformType::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
transform->SetParameters(initialParameters);
registration->SetInitialTransform(transform);
registration->InPlaceOn();
// 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
//
// Now we set the optimizer parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetNormalVariateGenerator(generator);
optimizer->Initialize(10);
optimizer->SetEpsilon(1.0);
optimizer->SetMaximumIteration(4000);
// Software Guide : EndCodeSnippet
// One level registration process without shrinking and smoothing.
//
constexpr 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 << "Registration completed!" << std::endl;
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cout << "ExceptionObject caught !" << std::endl;
std::cout << err << std::endl;
return EXIT_FAILURE;
}
ParametersType finalParameters = transform->GetParameters();
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
using 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);
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using 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;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itkImageFileReader.h
itk::SmartPointer< Self >
itkCastImageFilter.h
itkImageRegistrationMethodv4.h
itkTranslationTransform.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itkOnePlusOneEvolutionaryOptimizerv4.h
itk::Statistics::NormalVariateGenerator
Normal random variate generator.
Definition: itkNormalVariateGenerator.h:98
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itk::TranslationTransform
Translation transformation of a vector space (e.g. space coordinates)
Definition: itkTranslationTransform.h:43
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkImageFileWriter.h
itk::ImageRegistrationMethodv4
Interface method for the current registration framework.
Definition: itkImageRegistrationMethodv4.h:117
itkNormalVariateGenerator.h
itk::ResampleImageFilter
Resample an image via a coordinate transform.
Definition: itkResampleImageFilter.h:90
itk::Object
Base class for most ITK classes.
Definition: itkObject.h:62
itk::Math::e
static constexpr double e
Definition: itkMath.h:54
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:86
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition: itkEventObject.h:57
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itkMattesMutualInformationImageToImageMetricv4.h
itk::OnePlusOneEvolutionaryOptimizerv4
1+1 evolutionary strategy optimizer
Definition: itkOnePlusOneEvolutionaryOptimizerv4.h:70
itk::MattesMutualInformationImageToImageMetricv4
Computes the mutual information between two images to be registered using the method of Mattes et al.
Definition: itkMattesMutualInformationImageToImageMetricv4.h:103