ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration20.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 : BeginCommandLineArgs
// INPUTS: {brainweb1e1a10f20.mha}
// INPUTS: {brainweb1e1a10f20Rot10Tx15.mha}
// ARGUMENTS: ImageRegistration20Output.mhd
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{AffineTransform}
// for performing registration in $3D$.
//
// \index{itk::AffineTransform}
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// Let's start by including the header file of the AffineTransform.
//
// \index{itk::AffineTransform!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
//
// The following piece of code implements an observer
// that will 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() = 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 < 4)
{
std::cerr << "Missing Parameters " << std::endl;
std::cerr << "Usage: " << argv[0];
std::cerr << " fixedImageFile movingImageFile " << std::endl;
std::cerr << " outputImagefile [differenceBeforeRegistration] "
<< std::endl;
std::cerr << " [differenceAfterRegistration] " << std::endl;
std::cerr << " [stepLength] [maxNumberOfIterations] " << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We define then the types of the images to be registered.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
constexpr unsigned int Dimension = 3;
using PixelType = float;
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The transform type is instantiated using the code below. The template
// parameters of this class are the representation type of the space
// coordinates and the space dimension.
//
// \index{itk::AffineTransform!Instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
using MetricType =
using InterpolatorType =
using RegistrationType =
MetricType::Pointer metric = MetricType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
registration->SetInterpolator(interpolator);
// Software Guide : BeginLatex
//
// The transform object is constructed below and passed to the registration
// method.
//
// \index{itk::AffineTransform!New()}
// \index{itk::AffineTransform!Pointer}
// \index{itk::RegistrationMethod!SetTransform()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer transform = TransformType::New();
registration->SetTransform(transform);
// 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();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion());
// Software Guide : BeginLatex
//
// In this example, we again use the
// \doxygen{CenteredTransformInitializer} helper class in order to compute
// a reasonable value for the initial center of rotation and the
// translation. The initializer is set to use the center of mass of each
// image as the initial correspondence correction.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using TransformInitializerType =
FixedImageType,
MovingImageType>;
TransformInitializerType::Pointer initializer =
TransformInitializerType::New();
initializer->SetTransform(transform);
initializer->SetFixedImage(fixedImageReader->GetOutput());
initializer->SetMovingImage(movingImageReader->GetOutput());
initializer->MomentsOn();
initializer->InitializeTransform();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we pass the parameters of the current transform as the initial
// parameters to be used when the registration process starts.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
registration->SetInitialTransformParameters(transform->GetParameters());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Keeping in mind that the scale of units in scaling, rotation and
// translation are quite different, we take advantage of the scaling
// functionality provided by the optimizers. We know that the first $N
// \times N$ elements of the parameters array correspond to the rotation
// matrix factor, and the last $N$ are the components of the translation to
// be applied after multiplication with the matrix is performed.
//
// Software Guide : EndLatex
double translationScale = 1.0 / 1000.0;
if (argc > 8)
{
translationScale = std::stod(argv[8]);
}
// Software Guide : BeginCodeSnippet
using OptimizerScalesType = OptimizerType::ScalesType;
OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
optimizerScales[0] = 1.0;
optimizerScales[1] = 1.0;
optimizerScales[2] = 1.0;
optimizerScales[3] = 1.0;
optimizerScales[4] = 1.0;
optimizerScales[5] = 1.0;
optimizerScales[6] = 1.0;
optimizerScales[7] = 1.0;
optimizerScales[8] = 1.0;
optimizerScales[9] = translationScale;
optimizerScales[10] = translationScale;
optimizerScales[11] = translationScale;
optimizer->SetScales(optimizerScales);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We also set the usual parameters of the optimization method. In this
// case we are using an
// \doxygen{RegularStepGradientDescentOptimizer}. Below, we define the
// optimization parameters like initial step length, minimal step length
// and number of iterations. These last two act as stopping criteria for
// the optimization.
//
// Software Guide : EndLatex
double steplength = 0.1;
if (argc > 6)
{
steplength = std::stod(argv[6]);
}
unsigned int maxNumberOfIterations = 300;
if (argc > 7)
{
maxNumberOfIterations = std::stoi(argv[7]);
}
// Software Guide : BeginCodeSnippet
optimizer->SetMaximumStepLength(steplength);
optimizer->SetMinimumStepLength(0.0001);
optimizer->SetNumberOfIterations(maxNumberOfIterations);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We also set the optimizer to do minimization by calling the
// \code{MinimizeOn()} method.
//
// \index{itk::Regular\-Step\-Gradient\-Descent\-Optimizer!MinimizeOn()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->MinimizeOn();
// Software Guide : EndCodeSnippet
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
optimizer->AddObserver(itk::IterationEvent(), observer);
// Software Guide : BeginLatex
//
// Finally we trigger the execution of the registration method by calling
// the \code{Update()} method. The call is placed in a \code{try/catch}
// block in case any exceptions are thrown.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
registration->Update();
std::cout << "Optimizer stop condition: "
<< registration->GetOptimizer()->GetStopConditionDescription()
<< std::endl;
}
catch (const itk::ExceptionObject & err)
{
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
return EXIT_FAILURE;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Once the optimization converges, we recover the parameters from the
// registration method. This is done with the
// \code{GetLastTransformParameters()} method. We can also recover the
// final value of the metric with the \code{GetValue()} method and the
// final number of iterations with the \code{GetCurrentIteration()}
// method.
//
// \index{itk::RegistrationMethod!GetValue()}
// \index{itk::RegistrationMethod!GetCurrentIteration()}
// \index{itk::RegistrationMethod!GetLastTransformParameters()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
OptimizerType::ParametersType finalParameters =
registration->GetLastTransformParameters();
const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
const double bestValue = optimizer->GetValue();
// Software Guide : EndCodeSnippet
// Print out results
//
std::cout << "Result = " << std::endl;
std::cout << " Iterations = " << numberOfIterations << std::endl;
std::cout << " Metric value = " << bestValue << std::endl;
// The following code is used to dump output images to files.
// They illustrate the final results of the registration.
// We will resample the moving image and write out the difference image
// before and after registration. We will also rescale the intensities of
// the difference images, so that they look better!
using ResampleFilterType =
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters(finalParameters);
finalTransform->SetFixedParameters(transform->GetFixedParameters());
ResampleFilterType::Pointer resampler = ResampleFilterType::New();
resampler->SetTransform(finalTransform);
resampler->SetInput(movingImageReader->GetOutput());
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resampler->SetOutputOrigin(fixedImage->GetOrigin());
resampler->SetOutputSpacing(fixedImage->GetSpacing());
resampler->SetOutputDirection(fixedImage->GetDirection());
resampler->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(resampler->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
using DifferenceFilterType =
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(resampler->GetOutput());
WriterType::Pointer writer2 = WriterType::New();
using RescalerType =
RescalerType::Pointer intensityRescaler = RescalerType::New();
intensityRescaler->SetInput(difference->GetOutput());
intensityRescaler->SetOutputMinimum(0);
intensityRescaler->SetOutputMaximum(255);
writer2->SetInput(intensityRescaler->GetOutput());
resampler->SetDefaultPixelValue(1);
// Compute the difference image between the
// fixed and resampled moving image.
if (argc > 5)
{
writer2->SetFileName(argv[5]);
writer2->Update();
}
using IdentityTransformType = itk::IdentityTransform<double, Dimension>;
IdentityTransformType::Pointer identity = IdentityTransformType::New();
// Compute the difference image between the
// fixed and moving image before registration.
if (argc > 4)
{
resampler->SetTransform(identity);
writer2->SetFileName(argv[4]);
writer2->Update();
}
return EXIT_SUCCESS;
}
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itk::IdentityTransform
Implementation of an Identity Transform.
Definition: itkIdentityTransform.h:50
itkRegularStepGradientDescentOptimizer.h
itkCenteredTransformInitializer.h
itkImageFileReader.h
itk::ImageRegistrationMethod
Base class for Image Registration Methods.
Definition: itkImageRegistrationMethod.h:70
itk::SmartPointer< Self >
itkCastImageFilter.h
itkAffineTransform.h
itk::AffineTransform
Definition: itkAffineTransform.h:101
itk::RegularStepGradientDescentOptimizer
Implement a gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizer.h:33
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itkMeanSquaresImageToImageMetric.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
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itk::Command
class ITK_FORWARD_EXPORT Command
Definition: itkObject.h:43
itkImageRegistrationMethod.h
itkSubtractImageFilter.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkRescaleIntensityImageFilter.h
itk::SubtractImageFilter
Pixel-wise subtraction of two images.
Definition: itkSubtractImageFilter.h:68
itk::MeanSquaresImageToImageMetric
TODO.
Definition: itkMeanSquaresImageToImageMetric.h:39
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:62
itk::RescaleIntensityImageFilter
Applies a linear transformation to the intensity levels of the input Image.
Definition: itkRescaleIntensityImageFilter.h:154
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
itk::CenteredTransformInitializer
CenteredTransformInitializer is a helper class intended to initialize the center of rotation and the ...
Definition: itkCenteredTransformInitializer.h:61