ITK  5.4.0 Insight Toolkit
SphinxExamples/src/Registration/Common/PerformMultiModalityRegistrationWithMutualInformation/Code.cxx
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// The following simple example illustrates how multiple imaging modalities can
// be registered using the ITK registration framework. The first difference
// between this and previous examples is the use of the
// MutualInformationImageToImageMetric as the cost-function to be
// optimized. The second difference is the use of the
// GradientDescentOptimizer. Due to the stochastic nature of the
// metric computation, the values are too noisy to work successfully with the
// RegularStepGradientDescentOptimizer. Therefore, we will use the
// simpler GradientDescentOptimizer with a user defined learning rate. The
// following headers declare the basic components of this registration method.
// One way to simplify the computation of the mutual information is
// to normalize the statistical distribution of the two input images. The
// NormalizeImageFilter is the perfect tool for this task.
// It rescales the intensities of the input images in order to produce an
// output image with zero mean and unit variance.
// Additionally, low-pass filtering of the images to be registered will also
// increase robustness against noise. In this example, we will use the
// DiscreteGaussianImageFilter for that purpose.
// The following section of code implements a Command observer
// that will monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
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 = dynamic_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 < 4)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile";
std::cerr << " movingImageFile";
std::cerr << " outputImageFile ";
std::cerr << " [checkerBoardBefore]";
std::cerr << " [checkerBoardAfter]" << std::endl;
return EXIT_FAILURE;
}
const char * fixedImageFile = argv[1];
const char * movingImageFile = argv[2];
const char * outputImageFile = argv[3];
const char * checkerBoardBefore = argv[4];
const char * checkerBoardAfter = argv[5];
constexpr unsigned int Dimension = 2;
using PixelType = unsigned short;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// It is convenient to work with an internal image type because mutual
// information will perform better on images with a normalized statistical
// distribution. The fixed and moving images will be normalized and
// converted to this internal type.
using InternalPixelType = float;
using InternalImageType = itk::Image<InternalPixelType, Dimension>;
auto transform = TransformType::New();
auto optimizer = OptimizerType::New();
auto interpolator = InterpolatorType::New();
auto registration = RegistrationType::New();
auto metric = MetricType::New();
registration->SetOptimizer(optimizer);
registration->SetTransform(transform);
registration->SetInterpolator(interpolator);
registration->SetMetric(metric);
// The metric requires a number of parameters to be selected, including
// the standard deviation of the Gaussian kernel for the fixed image
// density estimate, the standard deviation of the kernel for the moving
// image density and the number of samples use to compute the densities
// and entropy values. Experience has
// shown that a kernel standard deviation of 0.4 works well for images
// which have been normalized to a mean of zero and unit variance. We
// will follow this empirical rule in this example.
metric->SetFixedImageStandardDeviation(0.4);
metric->SetMovingImageStandardDeviation(0.4);
auto fixedNormalizer = FixedNormalizeFilterType::New();
auto movingNormalizer = MovingNormalizeFilterType::New();
auto fixedSmoother = GaussianFilterType::New();
auto movingSmoother = GaussianFilterType::New();
fixedSmoother->SetVariance(2.0);
movingSmoother->SetVariance(2.0);
fixedNormalizer->SetInput(fixedImage);
movingNormalizer->SetInput(movingImage);
fixedSmoother->SetInput(fixedNormalizer->GetOutput());
movingSmoother->SetInput(movingNormalizer->GetOutput());
registration->SetFixedImage(fixedSmoother->GetOutput());
registration->SetMovingImage(movingSmoother->GetOutput());
fixedNormalizer->Update();
FixedImageType::RegionType fixedImageRegion = fixedNormalizer->GetOutput()->GetBufferedRegion();
registration->SetFixedImageRegion(fixedImageRegion);
using ParametersType = RegistrationType::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);
// We should now define the number of spatial samples to be considered in
// the metric computation. Note that we were forced to postpone this setting
// until we had done the preprocessing of the images because the number of
// samples is usually defined as a fraction of the total number of pixels in
// the fixed image.
//
// The number of spatial samples can usually be as low as $1\%$ of the total
// number of pixels in the fixed image. Increasing the number of samples
// improves the smoothness of the metric from one iteration to another and
// therefore helps when this metric is used in conjunction with optimizers
// that rely of the continuity of the metric values. The trade-off, of
// course, is that a larger number of samples result in longer computation
// times per every evaluation of the metric.
//
// It has been demonstrated empirically that the number of samples is not a
// critical parameter for the registration process. When you start fine
// tuning your own registration process, you should start using high values
// of number of samples, for example in the range of 20% to 50% of the
// number of pixels in the fixed image. Once you have succeeded to register
// your images you can then reduce the number of samples progressively until
// you find a good compromise on the time it takes to compute one evaluation
// of the Metric. Note that it is not useful to have very fast evaluations
// of the Metric if the noise in their values results in more iterations
// being required by the optimizer to converge.
// behavior of the metric values as the iterations progress.
const unsigned int numberOfPixels = fixedImageRegion.GetNumberOfPixels();
const auto numberOfSamples = static_cast<unsigned int>(numberOfPixels * 0.01);
metric->SetNumberOfSpatialSamples(numberOfSamples);
// For consistent results when regression testing.
metric->ReinitializeSeed(121212);
// Since larger values of mutual information indicate better matches than
// smaller values, we need to maximize the cost function in this example.
// By default the GradientDescentOptimizer class is set to minimize the
// value of the cost-function. It is therefore necessary to modify its
// default behavior by invoking the MaximizeOn() method.
// Additionally, we need to define the optimizer's step size using the
// SetLearningRate() method.
optimizer->SetNumberOfIterations(200);
optimizer->MaximizeOn();
// Note that large values of the learning rate will make the optimizer
// unstable. Small values, on the other hand, may result in the optimizer
// needing too many iterations in order to walk to the extrema of the cost
// function. The easy way of fine tuning this parameter is to start with
// small values, probably in the range of {5.0, 10.0}. Once the other
// registration parameters have been tuned for producing convergence, you
// may want to revisit the learning rate and start increasing its value until
// you observe that the optimization becomes unstable. The ideal value for
// this parameter is the one that results in a minimum number of iterations
// while still keeping a stable path on the parametric space of the
// optimization. Keep in mind that this parameter is a multiplicative factor
// applied on the gradient of the Metric. Therefore, its effect on the
// optimizer step length is proportional to the Metric values themselves.
// Metrics with large values will require you to use smaller values for the
// learning rate in order to maintain a similar optimizer behavior.
optimizer->SetLearningRate(15.0);
auto observer = CommandIterationUpdate::New();
try
{
registration->Update();
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 = registration->GetLastTransformParameters();
double TranslationAlongX = finalParameters[0];
double TranslationAlongY = finalParameters[1];
unsigned int numberOfIterations = optimizer->GetCurrentIteration();
double bestValue = optimizer->GetValue();
// Print out results
std::cout << std::endl;
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;
std::cout << " Numb. Samples = " << numberOfSamples << std::endl;
auto finalTransform = TransformType::New();
finalTransform->SetParameters(finalParameters);
finalTransform->SetFixedParameters(transform->GetFixedParameters());
auto resample = ResampleFilterType::New();
resample->SetTransform(finalTransform);
resample->SetInput(movingImage);
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>;
auto caster = CastFilterType::New();
caster->SetInput(resample->GetOutput());
itk::WriteImage(caster->GetOutput(), outputImageFile);
// Generate checkerboards before and after registration
using CheckerBoardFilterType = itk::CheckerBoardImageFilter<FixedImageType>;
auto checker = CheckerBoardFilterType::New();
checker->SetInput1(fixedImage);
checker->SetInput2(resample->GetOutput());
caster->SetInput(checker->GetOutput());
// Before registration
auto identityTransform = TransformType::New();
identityTransform->SetIdentity();
resample->SetTransform(identityTransform);
if (argc > 4)
{
itk::WriteImage(caster->GetOutput(), checkerBoardBefore);
}
// After registration
resample->SetTransform(finalTransform);
if (argc > 5)
{
itk::WriteImage(caster->GetOutput(), checkerBoardAfter);
}
return EXIT_SUCCESS;
}
Pointer
SmartPointer< Self > Pointer
itk::DiscreteGaussianImageFilter
Blurs an image by separable convolution with discrete gaussian kernels. This filter performs Gaussian...
Definition: itkDiscreteGaussianImageFilter.h:64
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:100
itk::CheckerBoardImageFilter
Combines two images in a checkerboard pattern.
Definition: itkCheckerBoardImageFilter.h:46
itk::ImageRegistrationMethod
Base class for Image Registration Methods.
Definition: itkImageRegistrationMethod.h:70
itk::SmartPointer< Self >
itkCastImageFilter.h
itkTranslationTransform.h
itkNormalizeImageFilter.h
itk::MutualInformationImageToImageMetric
Computes the mutual information between two images to be registered.
Definition: itkMutualInformationImageToImageMetric.h:94
itkMutualInformationImageToImageMetric.h
itk::LinearInterpolateImageFunction
Linearly interpolate an image at specified positions.
Definition: itkLinearInterpolateImageFunction.h:50
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itkCheckerBoardImageFilter.h
itk::NormalizeImageFilter
Normalize an image by setting its mean to zero and variance to one.
Definition: itkNormalizeImageFilter.h:54
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:42
itk::GTest::TypedefsAndConstructors::Dimension2::RegionType
ImageBaseType::RegionType RegionType
Definition: itkGTestTypedefsAndConstructors.h:54
itk::TranslationTransform
Translation transformation of a vector space (e.g. space coordinates)
Definition: itkTranslationTransform.h:43
itkImageRegistrationMethod.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkImageFileWriter.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:61
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
itk::EventObject
Abstraction of the Events used to communicating among filters and with GUIs.
Definition: itkEventObject.h:57
New
static Pointer New()
itk::ImageRegion::GetNumberOfPixels
SizeValueType GetNumberOfPixels() const
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itkDiscreteGaussianImageFilter.h
Superclass
BinaryGeneratorImageFilter< TInputImage1, TInputImage2, TOutputImage > Superclass