ITK  5.2.0
Insight Toolkit
Examples/RegistrationITKv4/MultiStageImageRegistration2.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: {BrainT1SliceBorder20.png}
// INPUTS: {BrainProtonDensitySliceR10X13Y17.png}
// OUTPUTS: {MultiStageImageRegistration2Output.png}
// ARGUMENTS: 100
// OUTPUTS: {MultiStageImageRegistration2CheckerboardBefore.png}
// OUTPUTS: {MultiStageImageRegistration2CheckerboardAfter.png}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This examples shows how different stages can be cascaded together directly
// in a multistage registration process. The example code is, for the most
// part, identical to the previous multistage example. The main difference
// is that no initial transform is used, and the output of the first stage
// is directly linked to the second stage, and the whole registration process
// is triggered only once by calling \code{Update()} after the last stage
// stage.
//
// We will focus on the most relevent changes in current code and skip all
// the similar parts already explained in the previous example.
//
// \index{itk::ImageRegistrationMethodv4!Multi-Stage}
//
// Software Guide : EndLatex
#include "itkCommand.h"
// The following section of code implements a Command observer
// that will monitor the configurations of the registration process
// at every change of stage and resolution level.
//
template <typename TRegistration>
class RegistrationInterfaceCommand : public itk::Command
{
public:
using Self = RegistrationInterfaceCommand;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
RegistrationInterfaceCommand() = default;
public:
using RegistrationType = TRegistration;
// The Execute function simply calls another version of the \code{Execute()}
// method accepting a \code{const} input object
void
Execute(itk::Object * object, const itk::EventObject & event) override
{
Execute((const itk::Object *)object, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
if (!(itk::MultiResolutionIterationEvent().CheckEvent(&event)))
{
return;
}
std::cout << "\nObserving from class " << object->GetNameOfClass();
if (!object->GetObjectName().empty())
{
std::cout << " \"" << object->GetObjectName() << "\"" << std::endl;
}
const auto * registration = static_cast<const RegistrationType *>(object);
if (registration == nullptr)
{
itkExceptionMacro(<< "Dynamic cast failed, object of type "
<< object->GetNameOfClass());
}
unsigned int currentLevel = registration->GetCurrentLevel();
typename RegistrationType::ShrinkFactorsPerDimensionContainerType
shrinkFactors =
registration->GetShrinkFactorsPerDimension(currentLevel);
typename RegistrationType::SmoothingSigmasArrayType smoothingSigmas =
registration->GetSmoothingSigmasPerLevel();
std::cout << "-------------------------------------" << std::endl;
std::cout << " Current multi-resolution level = " << currentLevel
<< std::endl;
std::cout << " shrink factor = " << shrinkFactors << std::endl;
std::cout << " smoothing sigma = " << smoothingSigmas[currentLevel]
<< std::endl;
std::cout << std::endl;
}
};
// The following section of code implements an observer
// that will monitor the evolution of the registration process.
//
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 (optimizer == nullptr)
{
return; // in this unlikely context, just do nothing.
}
if (!(itk::IterationEvent().CheckEvent(&event)))
{
return;
}
std::cout << optimizer->GetCurrentIteration() << " ";
std::cout << optimizer->GetValue() << " ";
std::cout << optimizer->GetCurrentPosition() << " "
<< m_CumulativeIterationIndex++ << std::endl;
}
private:
unsigned int m_CumulativeIterationIndex{ 0 };
};
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::cerr << " outputImagefile [backgroundGrayLevel]";
std::cerr << " [checkerboardbefore] [CheckerBoardAfter]";
std::cerr << " [numberOfBins] " << 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>;
// Software Guide : BeginLatex
//
// Let's start by defining different types of the first stage.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using MetricType =
MovingImageType>;
using TRegistrationType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Type definitions are the same as previous example with an important
// subtle change: the transform type is not passed to the registration
// method as a template parameter anymore. In this case, the registration
// filter will consider the transform base class \doxygen{Transform} as the
// type of its output transform.
//
// Software Guide : EndLatex
// All the components are instantiated using their \code{New()} method
// and connected to the registration object as in previous example.
//
TOptimizerType::Pointer transOptimizer = TOptimizerType::New();
MetricType::Pointer transMetric = MetricType::New();
TRegistrationType::Pointer transRegistration = TRegistrationType::New();
transRegistration->SetOptimizer(transOptimizer);
transRegistration->SetMetric(transMetric);
// Software Guide : BeginLatex
//
// Instead of passing the transform type, we create an explicit
// instantiation of the transform object outside of the registration
// filter, and connect that to the registration object using the
// \code{SetInitialTransform()} method. Also, by calling \code{InPlaceOn()}
// method, this transform object will be the output transform of the
// registration filter or will be grafted to the output.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
TTransformType::Pointer translationTx = TTransformType::New();
transRegistration->SetInitialTransform(translationTx);
transRegistration->InPlaceOn();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Also, there is no initial transform defined for this example.
//
// Software Guide : EndLatex
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]);
transRegistration->SetFixedImage(fixedImageReader->GetOutput());
transRegistration->SetMovingImage(movingImageReader->GetOutput());
transRegistration->SetObjectName("TranslationRegistration");
// Software Guide : BeginLatex
//
// As in the previous example, the first stage is run using only one level
// of registration at a coarse resolution level. However, notice that we do
// not need to update the translation registration filter at this step
// since the output of this stage will be directly connected to the initial
// input of the next stage. Due to ITK's pipeline structure, when we call
// the \code{Update()} at the last stage, the first stage will be updated
// as well.
//
// Software Guide : EndLatex
constexpr unsigned int numberOfLevels1 = 1;
TRegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel1;
shrinkFactorsPerLevel1.SetSize(numberOfLevels1);
shrinkFactorsPerLevel1[0] = 3;
TRegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel1;
smoothingSigmasPerLevel1.SetSize(numberOfLevels1);
smoothingSigmasPerLevel1[0] = 2;
transRegistration->SetNumberOfLevels(numberOfLevels1);
transRegistration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel1);
transRegistration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel1);
transMetric->SetNumberOfHistogramBins(24);
if (argc > 7)
{
// optionally, override the values with numbers taken from the command
// line arguments.
transMetric->SetNumberOfHistogramBins(std::stoi(argv[7]));
}
transOptimizer->SetNumberOfIterations(200);
transOptimizer->SetRelaxationFactor(0.5);
transOptimizer->SetLearningRate(16);
transOptimizer->SetMinimumStepLength(1.5);
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer1 = CommandIterationUpdate::New();
transOptimizer->AddObserver(itk::IterationEvent(), observer1);
// Create the Command interface observer and register it with the optimizer.
//
using TranslationCommandType =
RegistrationInterfaceCommand<TRegistrationType>;
TranslationCommandType::Pointer command1 = TranslationCommandType::New();
transRegistration->AddObserver(itk::MultiResolutionIterationEvent(),
command1);
// Software Guide : BeginLatex
//
// Now we upgrade to an Affine transform as the second stage of
// registration process, and as before, we initially define and instantiate
// different components of the current registration stage. We have used a
// new optimizer but the same metric in new configurations.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using AOptimizerType =
using ARegistrationType =
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Again notice that \emph{TransformType} is not passed to the type
// definition of the registration filter. It is important because when the
// registration filter considers transform base class \doxygen{Transform}
// as the type of its output transform, it prevents the type mismatch when
// the two stages are cascaded to each other.
//
// Then, all components are instantiated using their \code{New()} method
// and connected to the registration object among the transform type.
// Despite the previous example, here we use the fixed image's center of
// mass to initialize the fixed parameters of the Affine transform.
// \doxygen{ImageMomentsCalculator} filter is used for this purpose.
//
// Software Guide : EndLatex
AOptimizerType::Pointer affineOptimizer = AOptimizerType::New();
MetricType::Pointer affineMetric = MetricType::New();
ARegistrationType::Pointer affineRegistration = ARegistrationType::New();
affineRegistration->SetOptimizer(affineOptimizer);
affineRegistration->SetMetric(affineMetric);
affineMetric->SetNumberOfHistogramBins(24);
if (argc > 7)
{
// optionally, override the values with numbers taken from the command
// line arguments.
affineMetric->SetNumberOfHistogramBins(std::stoi(argv[7]));
}
fixedImageReader->Update();
FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
// Software Guide : BeginCodeSnippet
using FixedImageCalculatorType =
FixedImageCalculatorType::Pointer fixedCalculator =
FixedImageCalculatorType::New();
fixedCalculator->SetImage(fixedImage);
fixedCalculator->Compute();
fixedCalculator->GetCenterOfGravity();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Then, we initialize the fixed parameters (center of rotation) in the
// Affine transform and connect that to the registration object.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ATransformType::Pointer affineTx = ATransformType::New();
const unsigned int numberOfFixedParameters =
affineTx->GetFixedParameters().Size();
ATransformType::ParametersType fixedParameters(numberOfFixedParameters);
for (unsigned int i = 0; i < numberOfFixedParameters; ++i)
{
fixedParameters[i] = fixedCenter[i];
}
affineTx->SetFixedParameters(fixedParameters);
affineRegistration->SetInitialTransform(affineTx);
affineRegistration->InPlaceOn();
// Software Guide : EndCodeSnippet
affineRegistration->SetFixedImage(fixedImageReader->GetOutput());
affineRegistration->SetMovingImage(movingImageReader->GetOutput());
affineRegistration->SetObjectName("AffineRegistration");
// Software Guide : BeginLatex
//
// Now, the output of the first stage is wrapped through a
// \doxygen{DataObjectDecorator} and is passed to the input
// of the second stage as the moving initial transform via
// \code{SetMovingInitialTransformInput()} method. Note that
// this API has an ``Input'' word attached to the name of another
// initialization method \code{SetMovingInitialTransform()}
// that already has been used in previous example.
// This extension means that the following API expects
// a data object decorator type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
affineRegistration->SetMovingInitialTransformInput(
transRegistration->GetTransformOutput());
// Software Guide : EndCodeSnippet
using ScalesEstimatorType =
ScalesEstimatorType::Pointer scalesEstimator = ScalesEstimatorType::New();
scalesEstimator->SetMetric(affineMetric);
scalesEstimator->SetTransformForward(true);
affineOptimizer->SetScalesEstimator(scalesEstimator);
affineOptimizer->SetDoEstimateLearningRateOnce(true);
affineOptimizer->SetDoEstimateLearningRateAtEachIteration(false);
affineOptimizer->SetLowerLimit(0);
affineOptimizer->SetUpperLimit(2);
affineOptimizer->SetEpsilon(0.2);
affineOptimizer->SetNumberOfIterations(200);
affineOptimizer->SetMinimumConvergenceValue(1e-6);
affineOptimizer->SetConvergenceWindowSize(10);
// Create the Command observer and register it with the optimizer.
//
CommandIterationUpdate::Pointer observer2 = CommandIterationUpdate::New();
affineOptimizer->AddObserver(itk::IterationEvent(), observer2);
// Software Guide : BeginLatex
//
// Second stage runs two levels of registration, where the second
// level is run in full resolution.
//
// Software Guide : EndLatex
constexpr unsigned int numberOfLevels2 = 2;
ARegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel2;
shrinkFactorsPerLevel2.SetSize(numberOfLevels2);
shrinkFactorsPerLevel2[0] = 2;
shrinkFactorsPerLevel2[1] = 1;
ARegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel2;
smoothingSigmasPerLevel2.SetSize(numberOfLevels2);
smoothingSigmasPerLevel2[0] = 1;
smoothingSigmasPerLevel2[1] = 0;
affineRegistration->SetNumberOfLevels(numberOfLevels2);
affineRegistration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel2);
affineRegistration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel2);
// Create the Command interface observer and register it with the optimizer.
//
using AffineCommandType = RegistrationInterfaceCommand<ARegistrationType>;
AffineCommandType::Pointer command2 = AffineCommandType::New();
affineRegistration->AddObserver(itk::MultiResolutionIterationEvent(),
command2);
// Software Guide : BeginLatex
//
// Once all the registration components are in place,
// finally we trigger the whole registration process, including two
// cascaded registration stages, by calling \code{Update()} on the
// registration filter of the last stage, which causes both stages be
// updated.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
affineRegistration->Update();
std::cout
<< "Optimizer stop condition: "
<< affineRegistration->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
//
// Finally, a composite transform is used to concatenate the results of
// all stages together, which will be considered as the
// final output of this multistage process and will be passed to the
// resampler to resample the moving image into the virtual domain
// space (fixed image space if there is no fixed initial transform).
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using CompositeTransformType = itk::CompositeTransform<double, Dimension>;
CompositeTransformType::Pointer compositeTransform =
CompositeTransformType::New();
compositeTransform->AddTransform(translationTx);
compositeTransform->AddTransform(affineTx);
// Software Guide : EndCodeSnippet
std::cout << " Translation transform parameters after registration: "
<< std::endl
<< transOptimizer->GetCurrentPosition() << std::endl
<< " Last LearningRate: "
<< transOptimizer->GetCurrentStepLength() << std::endl;
std::cout << " Affine transform parameters after registration: "
<< std::endl
<< affineOptimizer->GetCurrentPosition() << std::endl
<< " Last LearningRate: " << affineOptimizer->GetLearningRate()
<< std::endl;
// Software Guide : BeginLatex
//
// Let's execute this example using the same multi-modality images as
// before. The registration converges after $6$ iterations in the first
// stage, also in $45$ and $11$ iterations corresponding to the first level
// and second level of the Affine stage.
// The final results when printed as an array of parameters are:
//
// \begin{verbatim}
// Translation parameters after first registration stage:
// [11.600, 15.1814]
//
// Affine parameters after second registration stage:
// [0.9860, -0.1742, 0.1751, 0.9862, 0.9219, 0.8023]
// \end{verbatim}
//
// Let's reorder the Affine array of parameters again as coefficients of
// matrix
// $\bf{M}$ and vector $\bf{T}$. They can now be seen as
//
// \begin{equation}
// M =
// \left[
// \begin{array}{cc}
// 0.9860 & -0.1742 \\ 0.1751 & 0.9862 \\ \end{array}
// \right]
// \mbox{ and }
// T =
// \left[
// \begin{array}{c}
// 0.9219 \\ 0.8023 \\ \end{array}
// \right]
// \end{equation}
//
// $10.02$ degrees is the rotation value computed from the affine matrix
// parameters, which approximately equals the intentional misalignment.
//
// Also for the total translation value resulted from both transforms, we
// have:
//
// In $X$ direction:
// \begin{equation}
// 11.6004 + 0.9219 = 12.5223
// \end{equation}
// In $Y$ direction:
// \begin{equation}
// 15.1814 + 0.8023 = 15.9837
// \end{equation}
//
// These results closely match the true misalignment introduced in the
// moving image.
//
// Software Guide : EndLatex
using ResampleFilterType =
ResampleFilterType::Pointer resample = ResampleFilterType::New();
resample->SetTransform(compositeTransform);
resample->SetInput(movingImageReader->GetOutput());
PixelType backgroundGrayLevel = 100;
if (argc > 4)
{
backgroundGrayLevel = std::stoi(argv[4]);
}
resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resample->SetOutputOrigin(fixedImage->GetOrigin());
resample->SetOutputSpacing(fixedImage->GetSpacing());
resample->SetOutputDirection(fixedImage->GetDirection());
resample->SetDefaultPixelValue(backgroundGrayLevel);
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();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.32\textwidth]{MultiStageImageRegistration2Output}
// \includegraphics[width=0.32\textwidth]{MultiStageImageRegistration2CheckerboardBefore}
// \includegraphics[width=0.32\textwidth]{MultiStageImageRegistration2CheckerboardAfter}
// \itkcaption[Multistage registration input images]{Mapped moving image
// (left) and composition of fixed and moving images before (center) and
// after (right) registration.}
// \label{fig:MultiStageImageRegistration2Outputs}
// \end{figure}
//
// The result of resampling the moving image is presented in the left image
// of Figure \ref{fig:MultiStageImageRegistration2Outputs}. The center and
// right images of the figure depict a checkerboard composite of the fixed
// and moving images before and after registration.
//
// Software Guide : EndLatex
//
// Generate checkerboards before and after registration
//
using CheckerBoardFilterType = itk::CheckerBoardImageFilter<FixedImageType>;
CheckerBoardFilterType::Pointer checker = CheckerBoardFilterType::New();
checker->SetInput1(fixedImage);
checker->SetInput2(resample->GetOutput());
caster->SetInput(checker->GetOutput());
writer->SetInput(caster->GetOutput());
resample->SetDefaultPixelValue(0);
// Write out checkerboard outputs
// Before registration
TransformType::Pointer identityTransform;
try
{
identityTransform = TransformType::New();
}
catch (const itk::ExceptionObject & err)
{
err.Print(std::cerr);
return EXIT_FAILURE;
}
identityTransform->SetIdentity();
resample->SetTransform(identityTransform);
if (argc > 5)
{
writer->SetFileName(argv[5]);
writer->Update();
}
// After registration
resample->SetTransform(compositeTransform);
if (argc > 6)
{
writer->SetFileName(argv[6]);
writer->Update();
}
return EXIT_SUCCESS;
}
itk::Object::GetObjectName
virtual const std::string & GetObjectName() const
itk::CastImageFilter
Casts input pixels to output pixel type.
Definition: itkCastImageFilter.h:104
itk::CompositeTransform
This class contains a list of transforms and concatenates them by composition.
Definition: itkCompositeTransform.h:87
itkRegularStepGradientDescentOptimizerv4.h
itk::IdentityTransform
Implementation of an Identity Transform.
Definition: itkIdentityTransform.h:50
itk::GradientDescentOptimizerv4Template
Gradient descent optimizer.
Definition: itkGradientDescentOptimizerv4.h:78
itk::GTest::TypedefsAndConstructors::Dimension2::VectorType
ImageBaseType::SpacingType VectorType
Definition: itkGTestTypedefsAndConstructors.h:53
itkImageFileReader.h
itk::CheckerBoardImageFilter
Combines two images in a checkerboard pattern.
Definition: itkCheckerBoardImageFilter.h:46
itk::SmartPointer< Self >
itkCastImageFilter.h
itkAffineTransform.h
itk::AffineTransform
Definition: itkAffineTransform.h:101
itkImageRegistrationMethodv4.h
itkTranslationTransform.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::RegularStepGradientDescentOptimizerv4
Regular Step Gradient descent optimizer.
Definition: itkRegularStepGradientDescentOptimizerv4.h:47
itk::Command
Superclass for callback/observer methods.
Definition: itkCommand.h:45
itkCheckerBoardImageFilter.h
itk::ImageFileWriter
Writes image data to a single file.
Definition: itkImageFileWriter.h:87
itkImageMomentsCalculator.h
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
itk::Object::GetNameOfClass
virtual const char * GetNameOfClass() const
itkImageFileWriter.h
itk::ImageRegistrationMethodv4
Interface method for the current registration framework.
Definition: itkImageRegistrationMethodv4.h:117
itk::ImageMomentsCalculator
Compute moments of an n-dimensional image.
Definition: itkImageMomentsCalculator.h:60
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::RegistrationParameterScalesFromPhysicalShift
Registration helper class for estimating scales of transform parameters a step sizes,...
Definition: itkRegistrationParameterScalesFromPhysicalShift.h:35
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
itkCompositeTransform.h
itkResampleImageFilter.h
itk::ConjugateGradientLineSearchOptimizerv4Template
Conjugate gradient descent optimizer with a golden section line search for nonlinear optimization.
Definition: itkConjugateGradientLineSearchOptimizerv4.h:48
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44
itkCommand.h
itkMattesMutualInformationImageToImageMetricv4.h
itkConjugateGradientLineSearchOptimizerv4.h
itk::MattesMutualInformationImageToImageMetricv4
Computes the mutual information between two images to be registered using the method of Mattes et al.
Definition: itkMattesMutualInformationImageToImageMetricv4.h:103