ITK  5.4.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration18.cxx
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*
* Copyright NumFOCUS
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* 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
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*
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
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// Software Guide : BeginLatex
//
// This example illustrates how to use the
// \doxygen{GradientDifferenceImageToImageMetric}.
//
// This metric is particularly useful for registration scenarios where fitting
// the edges of both images is the most relevant criteria for registration
// success.
//
// \index{itk::ImageRegistrationMethod!Monitoring}
//
//
// Software Guide : EndLatex
#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 = 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;
std::cerr << "[initialTx] [initialTy]" << std::endl;
return EXIT_FAILURE;
}
constexpr unsigned int Dimension = 2;
using PixelType = unsigned short;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
using InterpolatorType =
using RegistrationType =
using MetricType =
MovingImageType>;
auto transform = TransformType::New();
auto optimizer = OptimizerType::New();
auto interpolator = InterpolatorType::New();
auto registration = RegistrationType::New();
registration->SetOptimizer(optimizer);
registration->SetTransform(transform);
registration->SetInterpolator(interpolator);
auto metric = MetricType::New();
metric->SetDerivativeDelta(0.5);
registration->SetMetric(metric);
using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
auto fixedImageReader = FixedImageReaderType::New();
auto movingImageReader = MovingImageReaderType::New();
fixedImageReader->SetFileName(argv[1]);
movingImageReader->SetFileName(argv[2]);
registration->SetFixedImage(fixedImageReader->GetOutput());
registration->SetMovingImage(movingImageReader->GetOutput());
fixedImageReader
->Update(); // This is needed to make the BufferedRegion below valid.
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion());
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
if (argc > 4)
{
initialParameters[0] = std::stod(argv[4]);
}
if (argc > 5)
{
initialParameters[1] = std::stod(argv[5]);
}
std::cout << "Initial parameters = " << initialParameters << std::endl;
registration->SetInitialTransformParameters(initialParameters);
optimizer->SetMaximumStepLength(4.00);
optimizer->SetMinimumStepLength(0.01);
optimizer->SetNumberOfIterations(200);
optimizer->SetGradientMagnitudeTolerance(1e-40);
optimizer->MaximizeOn();
auto observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
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();
const double TranslationAlongX = finalParameters[0];
const double TranslationAlongY = finalParameters[1];
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
std::cout << "Registration done !" << std::endl;
std::cout << "Optimizer stop condition = "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
std::cout << "Number of iterations = " << numberOfIterations << std::endl;
std::cout << "Translation along X = " << TranslationAlongX << std::endl;
std::cout << "Translation along Y = " << TranslationAlongY << std::endl;
std::cout << "Optimal metric value = " << bestValue << std::endl;
// Prepare the resampling filter in order to map the moving image.
//
using ResampleFilterType =
auto finalTransform = TransformType::New();
finalTransform->SetParameters(finalParameters);
finalTransform->SetFixedParameters(transform->GetFixedParameters());
auto 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);
// Prepare a writer and caster filters to send the resampled moving image to
// a file
//
using OutputPixelType = unsigned char;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using CastFilterType =
auto writer = WriterType::New();
auto caster = CastFilterType::New();
writer->SetFileName(argv[3]);
caster->SetInput(resample->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
return EXIT_SUCCESS;
}
Pixel-wise addition of two images.
Casts input pixels to output pixel type.
Superclass for callback/observer methods.
Definition: itkCommand.h:46
virtual void Execute(Object *caller, const EventObject &event)=0
Abstraction of the Events used to communicating among filters and with GUIs.
Computes similarity between two objects to be registered.
Data source that reads image data from a single file.
Writes image data to a single file.
Base class for Image Registration Methods.
Templated n-dimensional image class.
Definition: itkImage.h:89
Linearly interpolate an image at specified positions.
Base class for most ITK classes.
Definition: itkObject.h:62
Resample an image via a coordinate transform.
Translation transformation of a vector space (e.g. space coordinates)
static Pointer New()
BinaryGeneratorImageFilter< TInputImage1, TInputImage2, TOutputImage > Superclass
SmartPointer< Self > Pointer
static constexpr double e
Definition: itkMath.h:56
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:42