ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/ImageRegistration10.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 the use of the image registration framework in
// Insight to align two label maps. Common structures are assumed to
// use the same label. The registration metric simply counts the
// number of corresponding pixels that have the same label.
//
//
// 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 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
{
auto optimizer = static_cast<OptimizerPointer>(object);
if (!itk::IterationEvent().CheckEvent(&event))
{
return;
}
std::cout << optimizer->GetCachedValue() << " ";
std::cout << optimizer->GetCachedCurrentPosition() << 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 [differenceImage]" << std::endl;
std::cerr << " [initialTx] [initialTy]" << std::endl;
return EXIT_FAILURE;
}
fow->SetInstance(fow);
// The types of each one of the components in the registration methods
// should be instantiated. First, we select the image dimension and the type
// for representing image pixels.
//
constexpr unsigned int Dimension = 2;
using PixelType = float;
// The types of the input images are instantiated by the following lines.
//
using FixedImageType = itk::Image<PixelType, Dimension>;
using MovingImageType = itk::Image<PixelType, Dimension>;
// Software Guide : BeginLatex
//
// The transform that will map one image space into the other is defined
// below.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// An optimizer is required to explore the parameter space of the transform
// in search of optimal values of the metric. The metric selected
// does not require analytical derivatives of its cost function.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OptimizerType = itk::AmoebaOptimizer;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The metric will compare how well the two images match each
// other. Metric types are usually parameterized by the image types
// as can be seen in the following type declaration. The metric
// selected here is suitable for comparing two label maps where the
// labels are consistent between the two maps. This metric
// measures the percentage of pixels that exactly match or
// mismatch.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MetricType =
// Software Guide : EndCodeSnippet
// Finally, the type of the interpolator is declared. The
// interpolator will evaluate the moving image at non-grid
// positions.
// Software Guide : BeginLatex
//
// Since we are registering label maps, we use a
// NearestNeighborInterpolateImageFunction to ensure subpixel
// values are not interpolated (to labels that do not exist).
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using InterpolatorType =
// Software Guide : EndCodeSnippet
// The registration method type is instantiated using the types of the
// fixed and moving images. This class is responsible for interconnecting
// all the components we have described so far.
using RegistrationType =
// Software Guide : BeginLatex
//
// Each one of the registration components is created using its
// \code{New()} method and is assigned to its respective
// \doxygen{SmartPointer}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
MetricType::Pointer metric = MetricType::New();
TransformType::Pointer transform = TransformType::New();
OptimizerType::Pointer optimizer = OptimizerType::New();
InterpolatorType::Pointer interpolator = InterpolatorType::New();
RegistrationType::Pointer registration = RegistrationType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We are using a MatchCardinalityImageToImageMetric to compare two
// label maps. This metric simple counts the percentage of
// corresponding pixels that have the same label. This metric does
// not provide analytical derivatives, so we will use an
// AmoebaOptimizer to drive the registration. The AmoebaOptimizer
// can only minimize a cost function, so we set the metric to count
// the percentages of mismatches.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
metric->MeasureMatchesOff();
// Software Guide : EndCodeSnippet
// Each component is now connected to the instance of the registration
// method. \index{itk::RegistrationMethod!SetMetric()}
// \index{itk::RegistrationMethod!SetOptimizer()}
// \index{itk::RegistrationMethod!SetTransform()}
// \index{itk::RegistrationMethod!SetFixedImage()}
// \index{itk::RegistrationMethod!SetMovingImage()}
// \index{itk::RegistrationMethod!SetInterpolator()}
//
registration->SetMetric(metric);
registration->SetOptimizer(optimizer);
registration->SetTransform(transform);
registration->SetInterpolator(interpolator);
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]);
// In this example, the fixed and moving images are read from files. This
// requires the \doxygen{ImageRegistrationMethod} to acquire its inputs to
// the output of the readers.
//
registration->SetFixedImage(fixedImageReader->GetOutput());
registration->SetMovingImage(movingImageReader->GetOutput());
// Software Guide : BeginLatex
//
// The registration can be restricted to consider only a particular region
// of the fixed image as input to the metric computation. This region is
// defined by the \code{SetFixedImageRegion()} method. You could use this
// feature to reduce the computational time of the registration or to avoid
// unwanted objects present in the image affecting the registration
// outcome. In this example we use the full available content of the image.
// This region is identified by the \code{BufferedRegion} of the fixed
// image. Note that for this region to be valid the reader must first
// invoke its \code{Update()} method.
//
// \index{itk::ImageRegistrationMethod!SetFixedImageRegion()}
// \index{itk::Image!GetBufferedRegion()}
//
// Software Guide : EndLatex
fixedImageReader->Update();
movingImageReader->Update();
registration->SetFixedImageRegion(
fixedImageReader->GetOutput()->GetBufferedRegion());
// Software Guide : BeginLatex
//
// The parameters of the transform are initialized by passing them in an
// array. This can be used to setup an initial known correction of the
// misalignment. In this particular case, a translation transform is
// being used for the registration. The array of parameters for this
// transform is simply composed of the translation values along each
// dimension. Setting the values of the parameters to zero
// initializes the transform as an \emph{identity} transform. Note that the
// array constructor requires the number of elements as an argument.
//
// \index{itk::TranslationTransform!GetNumberOfParameters()}
// \index{itk::RegistrationMethod!SetInitialTransformParameters()}
//
// Software Guide : EndLatex
using ParametersType = RegistrationType::ParametersType;
ParametersType initialParameters(transform->GetNumberOfParameters());
double tx = 0.0;
double ty = 0.0;
if (argc > 6)
{
tx = std::stod(argv[5]);
ty = std::stod(argv[6]);
}
initialParameters[0] = tx; // Initial offset in mm along X
initialParameters[1] = ty; // Initial offset in mm along Y
registration->SetInitialTransformParameters(initialParameters);
// Software Guide : BeginLatex
//
// At this point the registration method is ready for execution. The
// optimizer is the component that drives the execution of the
// registration. However, the ImageRegistrationMethod class
// orchestrates the ensemble to make sure that everything is in place
// before control is passed to the optimizer.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// It is usually desirable to fine tune the parameters of the optimizer.
// Each optimizer has particular parameters that must be interpreted in the
// context of the optimization strategy it implements.
//
// 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(
transform->GetNumberOfParameters());
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
// 0.25 (which will be a quarter 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
// percentage of pixels that mismatch. So we set the function
// convergence to be 0.1%
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
optimizer->SetParametersConvergenceTolerance(0.25); // quarter pixel
optimizer->SetFunctionConvergenceTolerance(0.001); // 0.1%
// 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);
// Software Guide : BeginLatex
//
// The registration process is triggered by an invocation of the
// \code{Update()} method. If something goes wrong during the
// initialization or execution of the registration an exception will be
// thrown. We should therefore place the \code{Update()} method
// in a \code{try/catch} block as illustrated in the following lines.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
// print out the initial metric value. need to initialize the
// registration method to force all the connections to be established.
registration->Initialize();
std::cout << "Initial Metric value = "
<< metric->GetValue(initialParameters) << std::endl;
// run the registration
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;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In a real application, you may attempt to recover from the error in the
// catch block. Here we are simply printing out a message and then
// terminating the execution of the program.
//
// Software Guide : EndLatex
// Software Guide : BeginLatex
//
// The result of the registration process is an array of parameters that
// defines the spatial transformation in an unique way. This final result
// is obtained using the \code{GetLastTransformParameters()} method.
//
// \index{itk::RegistrationMethod!GetLastTransformParameters()}
//
// Software Guide : EndLatex
ParametersType finalParameters = registration->GetLastTransformParameters();
// Software Guide : BeginLatex
//
// In the case of the \doxygen{TranslationTransform}, there is a
// straightforward interpretation of the parameters. Each element of the
// array corresponds to a translation along one spatial dimension.
//
// Software Guide : EndLatex
const double TranslationAlongX = finalParameters[0];
const double TranslationAlongY = finalParameters[1];
// The optimizer can be queried for the actual number of iterations
// performed to reach convergence.
//
const unsigned int numberOfIterations =
optimizer->GetOptimizer()->get_num_evaluations();
// Software Guide : BeginLatex
//
// The value of the image metric corresponding to the last set of
// parameters can be obtained with the \code{GetValue()} method of the
// optimizer. Since the AmoebaOptimizer does not yet support a call
// to \code{GetValue()}, we will simply re-evaluate the metric at the
// final parameters.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const double bestValue = metric->GetValue(finalParameters);
// Software Guide : EndCodeSnippet
// 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
//
// It is common, as the last step of a registration task, to use the
// resulting transform to map the moving image into the fixed image space.
// This is easily done with the \doxygen{ResampleImageFilter}. Please
// refer to Section~\ref{sec:ResampleImageFilter} for details on the use
// of this filter. First, a ResampleImageFilter type is instantiated
// using the image types. It is convenient to use the fixed image type as
// the output type since it is likely that the transformed moving image
// will be compared with the fixed image.
//
// Software Guide : EndLatex
using ResampleFilterType =
// Software Guide : BeginLatex
//
// A transform of the same type used in the registration process should be
// created and initialized with the parameters resulting from the
// registration process.
//
// \index{itk::ImageRegistrationMethod!Resampling image}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TransformType::Pointer finalTransform = TransformType::New();
finalTransform->SetParameters(finalParameters);
finalTransform->SetFixedParameters(transform->GetFixedParameters());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then a resampling filter is created and the corresponding transform and
// moving image connected as inputs.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(finalTransform);
resample->SetInput(movingImageReader->GetOutput());
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// As described in Section \ref{sec:ResampleImageFilter}, the
// ResampleImageFilter requires additional parameters to be
// specified, in particular, the spacing, origin and size of the output
// image. The default pixel value is also set to the standard label
// for "unknown" or background. Finally, we need to set the
// interpolator to be the same type of interpolator as the
// registration method used (nearest neighbor).
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resample->SetOutputOrigin(fixedImage->GetOrigin());
resample->SetOutputSpacing(fixedImage->GetSpacing());
resample->SetOutputDirection(fixedImage->GetDirection());
resample->SetDefaultPixelValue(0);
resample->SetInterpolator(interpolator);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The output of the filter is passed to a writer that will store the
// image in a file. An \doxygen{CastImageFilter} is used to convert the
// pixel type of the resampled image to the final type used by the
// writer. The cast and writer filters are instantiated below.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using OutputPixelType = unsigned short;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using CastFilterType =
// Software Guide : EndCodeSnippet
// SoftwareGuide : BeginLatex
//
// The filters are created by invoking their \code{New()}
// method.
//
// SoftwareGuide : EndLatex
// SoftwareGuide : BeginCodeSnippet
WriterType::Pointer writer = WriterType::New();
CastFilterType::Pointer caster = CastFilterType::New();
// SoftwareGuide : EndCodeSnippet
writer->SetFileName(argv[3]);
// SoftwareGuide : BeginLatex
//
// The \code{Update()} method of the writer is invoked in order to trigger
// the execution of the pipeline.
//
// SoftwareGuide : EndLatex
// SoftwareGuide : BeginCodeSnippet
caster->SetInput(resample->GetOutput());
writer->SetInput(caster->GetOutput());
writer->Update();
// SoftwareGuide : EndCodeSnippet
// SoftwareGuide : BeginLatex
//
// The fixed image and the transformed moving image can easily be compared
// using the \code{SquaredDifferenceImageFilter}. This pixel-wise
// filter computes the squared value of the difference between homologous
// pixels of its input images.
//
// SoftwareGuide : EndLatex
// SoftwareGuide : BeginCodeSnippet
using DifferenceFilterType =
FixedImageType,
OutputImageType>;
DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
difference->SetInput1(fixedImageReader->GetOutput());
difference->SetInput2(resample->GetOutput());
// SoftwareGuide : EndCodeSnippet
// Its output can be passed to another writer.
//
WriterType::Pointer writer2 = WriterType::New();
writer2->SetInput(difference->GetOutput());
if (argc > 4)
{
writer2->SetFileName(argv[4]);
writer2->Update();
}
return EXIT_SUCCESS;
}
// SoftwareGuide : BeginLatex
//
// The example was run on two binary images. The first binary image was
// generated by running the confidence connected image filter (section
// \ref{sec:ConfidenceConnected}) on the MRI slice of the brain. The second
// was generated similarly after shifting the slice by 13 pixels horizontally
// and 17 pixels vertically. The Amoeba optimizer converged after 34
// iterations and produced the following results:
//
// \begin{verbatim}
// Translation X = 12.5
// Translation Y = 16.77
// \end{verbatim}
// These results are a close match to the true misalignment.
//
// SoftwareGuide : EndLatex
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itk::SquaredDifferenceImageFilter
Implements pixel-wise the computation of squared difference.
Definition: itkSquaredDifferenceImageFilter.h:88
itk::FileOutputWindow::New
static Pointer New()
itkImageFileReader.h
itk::ImageRegistrationMethod
Base class for Image Registration Methods.
Definition: itkImageRegistrationMethod.h:70
itk::SmartPointer< Self >
itkCastImageFilter.h
itkAmoebaOptimizer.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
itk::MatchCardinalityImageToImageMetric
Computes similarity between two objects to be registered.
Definition: itkMatchCardinalityImageToImageMetric.h:67
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
itkFileOutputWindow.h
itkImageRegistrationMethod.h
itk::Command::Execute
virtual void Execute(Object *caller, const EventObject &event)=0
itkNearestNeighborInterpolateImageFunction.h
itkImageFileWriter.h
itk::AmoebaOptimizer
Wrap of the vnl_amoeba algorithm.
Definition: itkAmoebaOptimizer.h:66
itkSquaredDifferenceImageFilter.h
itkMatchCardinalityImageToImageMetric.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::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
itk::NearestNeighborInterpolateImageFunction
Nearest neighbor interpolation of a scalar image.
Definition: itkNearestNeighborInterpolateImageFunction.h:39
itkResampleImageFilter.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h