An interface method to the modular ITKv4 registration framework. More...

#include <sitkImageRegistrationMethod.h>

Inheritance diagram for itk::simple::ImageRegistrationMethod:

Collaboration diagram for itk::simple::ImageRegistrationMethod:

Detailed Description

An interface method to the modular ITKv4 registration framework.

This interface method class encapsulates typical registration usage by incorporating all the necessary elements for performing a simple image registration between two images. This method also allows for multistage registration whereby each stage is characterized by possibly different transforms and different image metrics. For example, many users will want to perform a linear registration followed by deformable registration where both stages are performed in multiple levels. Each level can be characterized by:

the resolution of the virtual domain image (see below)
smoothing of the fixed and moving images

Multiple stages are handled by linking multiple instantiations of this class where the output transform is added to the optional composite transform input.

See also: itk::ImageRegistrationMethodv4; itk::ImageToImageMetricv4; itk::ObjectToObjectOptimizerBaseTemplate

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

Definition at line 87 of file sitkImageRegistrationMethod.h.

Classes
struct	EvaluateMemberFunctionAddressor

Public Types
enum	EstimateLearningRateType { Never , Once , EachIteration }

enum	MetricSamplingStrategyType { NONE , REGULAR , RANDOM }
	Sampling strategies for obtaining points. More...

using	Self = ImageRegistrationMethod

using	Superclass = ProcessObject

Public Types inherited from itk::simple::ProcessObject
using	Self = ProcessObject

Public Member Functions
Transform	Execute (const Image &fixed, const Image &moving)
	Optimize the configured registration problem.

unsigned int	GetCurrentLevel () const

uint64_t	GetMetricNumberOfValidPoints () const

const std::vector< double > &	GetMetricSamplingPercentagePerLevel () const
	Get the percentage of pixels used for metric evaluation.

double	GetMetricValue () const

std::string	GetName () const override

double	GetOptimizerConvergenceValue () const

unsigned int	GetOptimizerIteration () const

double	GetOptimizerLearningRate () const

std::vector< double >	GetOptimizerPosition () const

std::vector< double >	GetOptimizerScales () const
	Get the OptimizerScales.

std::string	GetOptimizerStopConditionDescription () const

	ImageRegistrationMethod ()

double	MetricEvaluate (const Image &fixed, const Image &moving)
	Get the value of the metric given the state of the method.

void	SetInitialTransformAsBSpline (BSplineTransform &transform, bool inPlace=true, const std::vector< unsigned int > &scaleFactors=std::vector< unsigned int >())
	Set an initial BSpline transform to optimize.

void	SetMetricAsANTSNeighborhoodCorrelation (unsigned int radius)
	Use normalized cross correlation using a small neighborhood for each voxel between two images, with speed optimizations for dense registration.

void	SetMetricAsCorrelation ()
	Use negative normalized cross correlation image metric.

void	SetMetricAsDemons (double intensityDifferenceThreshold=0.001)
	Use demons image metric.

void	SetMetricAsJointHistogramMutualInformation (unsigned int numberOfHistogramBins=20, double varianceForJointPDFSmoothing=1.5)
	Use mutual information between two images.

void	SetMetricAsMattesMutualInformation (unsigned int numberOfHistogramBins=50)
	Use the mutual information between two images to be registered using the method of Mattes et al.

void	SetMetricAsMeanSquares ()
	Use negative means squares image metric.

void	SetMetricFixedMask (const Image &binaryMask)
	Set an image mask in order to restrict the sampled points for the metric.

void	SetMetricMovingMask (const Image &binaryMask)
	Set an image mask in order to restrict the sampled points for the metric in the moving image space.

void	SetMetricSamplingStrategy (MetricSamplingStrategyType strategy)
	Set sampling strategy for sample generation.

void	SetOptimizerAsAmoeba (double simplexDelta, unsigned int numberOfIterations, double parametersConvergenceTolerance=1e-8, double functionConvergenceTolerance=1e-4, bool withRestarts=false)
	Set optimizer to Nelder-Mead downhill simplex algorithm.

void	SetOptimizerAsConjugateGradientLineSearch (double learningRate, unsigned int numberOfIterations, double convergenceMinimumValue=1e-6, unsigned int convergenceWindowSize=10, double lineSearchLowerLimit=0, double lineSearchUpperLimit=5.0, double lineSearchEpsilon=0.01, unsigned int lineSearchMaximumIterations=20, EstimateLearningRateType estimateLearningRate=Once, double maximumStepSizeInPhysicalUnits=0.0)
	Conjugate gradient descent optimizer with a golden section line search for nonlinear optimization.

void	SetOptimizerAsExhaustive (const std::vector< unsigned int > &numberOfSteps, double stepLength=1.0)
	Set the optimizer to sample the metric at regular steps.

void	SetOptimizerAsGradientDescent (double learningRate, unsigned int numberOfIterations, double convergenceMinimumValue=1e-6, unsigned int convergenceWindowSize=10, EstimateLearningRateType estimateLearningRate=Once, double maximumStepSizeInPhysicalUnits=0.0)
	Gradient descent optimizer.

void	SetOptimizerAsGradientDescentLineSearch (double learningRate, unsigned int numberOfIterations, double convergenceMinimumValue=1e-6, unsigned int convergenceWindowSize=10, double lineSearchLowerLimit=0, double lineSearchUpperLimit=5.0, double lineSearchEpsilon=0.01, unsigned int lineSearchMaximumIterations=20, EstimateLearningRateType estimateLearningRate=Once, double maximumStepSizeInPhysicalUnits=0.0)
	Gradient descent optimizer with a golden section line search.

void	SetOptimizerAsLBFGS2 (double solutionAccuracy=1e-5, unsigned int numberOfIterations=0, unsigned int hessianApproximateAccuracy=6, unsigned int deltaConvergenceDistance=0, double deltaConvergenceTolerance=1e-5, unsigned int lineSearchMaximumEvaluations=40, double lineSearchMinimumStep=1e-20, double lineSearchMaximumStep=1e20, double lineSearchAccuracy=1e-4)
	Limited memory Broyden Fletcher Goldfarb Shannon minimization without bounds.

void	SetOptimizerAsLBFGSB (double gradientConvergenceTolerance=1e-5, unsigned int numberOfIterations=500, unsigned int maximumNumberOfCorrections=5, unsigned int maximumNumberOfFunctionEvaluations=2000, double costFunctionConvergenceFactor=1e+7, double lowerBound=std::numeric_limits< double >::min(), double upperBound=std::numeric_limits< double >::max(), bool trace=false)
	Limited memory Broyden Fletcher Goldfarb Shannon minimization with simple bounds.

void	SetOptimizerAsOnePlusOneEvolutionary (unsigned int numberOfIterations=100, double epsilon=1.5e-4, double initialRadius=1.01, double growthFactor=-1.0, double shrinkFactor=-1.0, unsigned int seed=sitkWallClock)
	1+1 evolutionary optimizer strategy.

void	SetOptimizerAsPowell (unsigned int numberOfIterations=100, unsigned int maximumLineIterations=100, double stepLength=1, double stepTolerance=1e-6, double valueTolerance=1e-6)
	Powell optimization using Brent line search.

void	SetOptimizerAsRegularStepGradientDescent (double learningRate, double minStep, unsigned int numberOfIterations, double relaxationFactor=0.5, double gradientMagnitudeTolerance=1e-4, EstimateLearningRateType estimateLearningRate=Never, double maximumStepSizeInPhysicalUnits=0.0)
	Regular Step Gradient descent optimizer.

void	SetOptimizerScales (const std::vector< double > &scales)
	Manually set per parameter weighting for the transform parameters.

void	SetOptimizerScalesFromIndexShift (unsigned int centralRegionRadius=5, double smallParameterVariation=0.01)
	Estimate scales from maximum voxel shift in index space cause by parameter change.

void	SetOptimizerScalesFromJacobian (unsigned int centralRegionRadius=5)
	Estimate scales from Jacobian norms.

void	SetOptimizerScalesFromPhysicalShift (unsigned int centralRegionRadius=5, double smallParameterVariation=0.01)
	Estimating scales of transform parameters a step sizes, from the maximum voxel shift in physical space caused by a parameter change.

void	SetShrinkFactorsPerLevel (const std::vector< unsigned int > &shrinkFactors)
	Set the isotropic shrink factors for each level.

void	SetSmoothingSigmasPerLevel (const std::vector< double > &smoothingSigmas)
	Set the sigmas of Gaussian used for smoothing.

bool	StopRegistration ()

std::string	ToString () const override
	Print the information about the object to a string.

	~ImageRegistrationMethod () override


InterpolatorEnum	GetInterpolator ()
	Set and get the interpolator to use.

void	SetInterpolator (InterpolatorEnum Interpolator)
	Set and get the interpolator to use.


void	SetInitialTransform (const Transform &transform)
	Set the initial transform and parameters to optimize.

void	SetInitialTransform (Transform &transform, bool inPlace=true)
	Set the initial transform and parameters to optimize.

Transform	GetInitialTransform ()
	Set the initial transform and parameters to optimize.

bool	GetInitialTransformInPlace () const
	Set the initial transform and parameters to optimize.


void	SetMovingInitialTransform (const Transform &transform)
	Set a fixed transform component towards moving domain.

Transform	GetMovingInitialTransform () const
	Set a fixed transform component towards moving domain.


void	SetFixedInitialTransform (const Transform &transform)
	Set transform mapping to the fixed domain.

Transform	GetFixedInitialTransform () const
	Set transform mapping to the fixed domain.


void	SetVirtualDomain (const std::vector< uint32_t > &virtualSize, const std::vector< double > &virtualOrigin, const std::vector< double > &virtualSpacing, const std::vector< double > &virtualDirection)
	Set the virtual domain used for sampling.

void	SetVirtualDomainFromImage (const Image &virtualImage)
	Set the virtual domain used for sampling.


void	SetOptimizerWeights (const std::vector< double > &weights)
	A per parameter weighting array for the optimizer.

std::vector< double >	GetOptimizerWeights () const
	A per parameter weighting array for the optimizer.


void	SetMetricSamplingPercentage (double percentage, unsigned int seed=sitkWallClock)
	Set percentage of pixels sampled for metric evaluation.

void	SetMetricSamplingPercentagePerLevel (const std::vector< double > &percentage, unsigned int seed=sitkWallClock)
	Set percentage of pixels sampled for metric evaluation.


void	SetMetricUseFixedImageGradientFilter (bool)
	Enable image gradient computation by a filter.

void	MetricUseFixedImageGradientFilterOn ()
	Enable image gradient computation by a filter.

void	MetricUseFixedImageGradientFilterOff ()
	Enable image gradient computation by a filter.


void	SetMetricUseMovingImageGradientFilter (bool)
	Enable image gradient computation by a filter.

void	MetricUseMovingImageGradientFilterOn ()
	Enable image gradient computation by a filter.

void	MetricUseMovingImageGradientFilterOff ()
	Enable image gradient computation by a filter.


void	SetSmoothingSigmasAreSpecifiedInPhysicalUnits (bool arg)
	Enable the smoothing sigmas for each level in physical units (default) or in terms of voxels.

void	SmoothingSigmasAreSpecifiedInPhysicalUnitsOn ()
	Enable the smoothing sigmas for each level in physical units (default) or in terms of voxels.

void	SmoothingSigmasAreSpecifiedInPhysicalUnitsOff ()
	Enable the smoothing sigmas for each level in physical units (default) or in terms of voxels.

Public Member Functions inherited from itk::simple::ProcessObject
virtual void	Abort ()

virtual int	AddCommand (itk::simple::EventEnum event, const std::function< void()> &func)
	Directly add a callback to observe an event.

virtual int	AddCommand (itk::simple::EventEnum event, itk::simple::Command &cmd)
	Add a Command Object to observer the event.

virtual float	GetProgress () const
	An Active Measurement of the progress of execution.

virtual bool	HasCommand (itk::simple::EventEnum event) const
	Query of this object has any registered commands for event.

	ProcessObject ()

virtual void	RemoveAllCommands ()
	Remove all registered commands.

virtual	~ProcessObject ()

virtual void	DebugOn ()

virtual void	DebugOff ()

virtual bool	GetDebug () const

virtual void	SetDebug (bool debugFlag)

virtual void	SetNumberOfThreads (unsigned int n)

virtual unsigned int	GetNumberOfThreads () const

virtual void	SetNumberOfWorkUnits (unsigned int n)

virtual unsigned int	GetNumberOfWorkUnits () const

Protected Member Functions
unsigned long	AddITKObserver (const itk::EventObject &, itk::Command *) override

template<class TImageType>
itk::ImageToImageMetricv4< TImageType, TImageType, TImageType, double, itk::DefaultImageToImageMetricTraitsv4< TImageType, TImageType, TImageType, double > > *	CreateMetric ()

itk::ObjectToObjectOptimizerBaseTemplate< double > *	CreateOptimizer (unsigned int numberOfTransformParameters)

template<typename TMetric>
itk::RegistrationParameterScalesEstimator< TMetric > *	CreateScalesEstimator ()

template<unsigned int VDimension>
itk::SpatialObject< VDimension > *	CreateSpatialObjectMask (const Image &mask)

template<typename TTransformAdaptorPointer, typename TRegistrationMethod>
std::vector< TTransformAdaptorPointer >	CreateTransformParametersAdaptor (TRegistrationMethod *method)

template<class TImage>
double	EvaluateInternal (const Image &fixed, const Image &moving)

template<class TImage>
Transform	ExecuteInternal (const Image &fixed, const Image &moving)

void	OnActiveProcessDelete () noexcept override

void	PreUpdate (itk::ProcessObject *p) override

void	RemoveITKObserver (EventCommand &e) override

template<class TImageType>
void	SetupMetric (itk::ImageToImageMetricv4< TImageType, TImageType, TImageType, double, itk::DefaultImageToImageMetricTraitsv4< TImageType, TImageType, TImageType, double > > , const TImageType , const TImageType *)

Protected Member Functions inherited from itk::simple::ProcessObject
virtual itk::ProcessObject *	GetActiveProcess ()

virtual void	onCommandDelete (const itk::simple::Command *cmd) noexcept

Protected Member Functions inherited from itk::simple::NonCopyable
	NonCopyable ()=default

	NonCopyable (const NonCopyable &)=delete

NonCopyable &	operator= (const NonCopyable &)=delete

Private Types
typedef double(ImageRegistrationMethod::*	EvaluateMemberFunctionType) (const Image &fixed, const Image &moving)

typedef Transform(ImageRegistrationMethod::*	MemberFunctionType) (const Image &fixed, const Image &moving)

enum	MetricType { ANTSNeighborhoodCorrelation , Correlation , Demons , JointHistogramMutualInformation , MeanSquares , MattesMutualInformation }

enum	OptimizerScalesType { Manual , Jacobian , IndexShift , PhysicalShift }

enum	OptimizerType { ConjugateGradientLineSearch , RegularStepGradientDescent , GradientDescent , GradientDescentLineSearch , LBFGSB , Exhaustive , Amoeba , Powell , OnePlusOneEvolutionary , LBFGS2 }

Private Attributes
itk::ObjectToObjectOptimizerBaseTemplate< double > *	m_ActiveOptimizer

Transform	m_FixedInitialTransform

Transform	m_InitialTransform

bool	m_InitialTransformInPlace

InterpolatorEnum	m_Interpolator

unsigned int	m_Iteration

Image	m_MetricFixedMaskImage

double	m_MetricIntensityDifferenceThreshold

Image	m_MetricMovingMaskImage

unsigned int	m_MetricNumberOfHistogramBins

unsigned int	m_MetricRadius

std::vector< double >	m_MetricSamplingPercentage

unsigned int	m_MetricSamplingSeed

MetricSamplingStrategyType	m_MetricSamplingStrategy

MetricType	m_MetricType

bool	m_MetricUseFixedImageGradientFilter

bool	m_MetricUseMovingImageGradientFilter

double	m_MetricValue

double	m_MetricVarianceForJointPDFSmoothing

Transform	m_MovingInitialTransform

uint64_t	m_NumberOfValidPoints

double	m_OptimizerConvergenceMinimumValue

unsigned int	m_OptimizerConvergenceWindowSize

double	m_OptimizerCostFunctionConvergenceFactor

unsigned int	m_OptimizerDeltaConvergenceDistance

double	m_OptimizerDeltaConvergenceTolerance

double	m_OptimizerEpsilon

EstimateLearningRateType	m_OptimizerEstimateLearningRate

double	m_OptimizerFunctionConvergenceTolerance

double	m_OptimizerGradientConvergenceTolerance

double	m_OptimizerGradientMagnitudeTolerance

double	m_OptimizerGrowthFactor

unsigned int	m_OptimizerHessianApproximationAccuracy

double	m_OptimizerInitialRadius

double	m_OptimizerLearningRate

double	m_OptimizerLineSearchAccuracy

double	m_OptimizerLineSearchEpsilon

double	m_OptimizerLineSearchLowerLimit

unsigned int	m_OptimizerLineSearchMaximumEvaluations

unsigned int	m_OptimizerLineSearchMaximumIterations

double	m_OptimizerLineSearchMaximumStep

double	m_OptimizerLineSearchMinimumStep

double	m_OptimizerLineSearchUpperLimit

double	m_OptimizerLowerBound

unsigned int	m_OptimizerMaximumLineIterations

unsigned int	m_OptimizerMaximumNumberOfCorrections

unsigned int	m_OptimizerMaximumNumberOfFunctionEvaluations

double	m_OptimizerMaximumStepSizeInPhysicalUnits

double	m_OptimizerMinimumStepLength

unsigned int	m_OptimizerNumberOfIterations

std::vector< unsigned int >	m_OptimizerNumberOfSteps

double	m_OptimizerParametersConvergenceTolerance

double	m_OptimizerRelaxationFactor

std::vector< double >	m_OptimizerScales

unsigned int	m_OptimizerScalesCentralRegionRadius

double	m_OptimizerScalesSmallParameterVariation

OptimizerScalesType	m_OptimizerScalesType

unsigned int	m_OptimizerSeed

double	m_OptimizerShrinkFactor

double	m_OptimizerSimplexDelta

double	m_OptimizerSolutionAccuracy

double	m_OptimizerStepLength

double	m_OptimizerStepTolerance

bool	m_OptimizerTrace

OptimizerType	m_OptimizerType

double	m_OptimizerUpperBound

double	m_OptimizerValueTolerance

std::vector< double >	m_OptimizerWeights

bool	m_OptimizerWithRestarts

std::function< unsigned int()>	m_pfGetCurrentLevel

std::function< uint64_t()>	m_pfGetMetricNumberOfValidPoints

std::function< double()>	m_pfGetMetricValue

std::function< double()>	m_pfGetOptimizerConvergenceValue

std::function< unsigned int()>	m_pfGetOptimizerIteration

std::function< double()>	m_pfGetOptimizerLearningRate

std::function< std::vector< double >()>	m_pfGetOptimizerPosition

std::function< std::vector< double >()>	m_pfGetOptimizerScales

std::function< std::string()>	m_pfGetOptimizerStopConditionDescription

std::function< void()>	m_pfOptimizerStopRegistration

std::function< void(itk::TransformBase *outTransform)>	m_pfUpdateWithBestValue

std::vector< unsigned int >	m_ShrinkFactorsPerLevel

bool	m_SmoothingSigmasAreSpecifiedInPhysicalUnits

std::vector< double >	m_SmoothingSigmasPerLevel

std::string	m_StopConditionDescription

std::vector< unsigned int >	m_TransformBSplineScaleFactors

std::vector< double >	m_VirtualDomainDirection

std::vector< double >	m_VirtualDomainOrigin

std::vector< uint32_t >	m_VirtualDomainSize

std::vector< double >	m_VirtualDomainSpacing

Friends
struct	detail::MemberFunctionAddressor< MemberFunctionType >

Additional Inherited Members
Static Public Member Functions inherited from itk::simple::ProcessObject
static bool	GetGlobalDefaultDebug ()

static void	GlobalDefaultDebugOff ()

static void	GlobalDefaultDebugOn ()

static void	SetGlobalDefaultDebug (bool debugFlag)

static void	GlobalWarningDisplayOn ()

static void	GlobalWarningDisplayOff ()

static void	SetGlobalWarningDisplay (bool flag)

static bool	GetGlobalWarningDisplay ()

static double	GetGlobalDefaultCoordinateTolerance ()
	Access the global tolerance to determine congruent spaces.

static void	SetGlobalDefaultCoordinateTolerance (double)
	Access the global tolerance to determine congruent spaces.

static double	GetGlobalDefaultDirectionTolerance ()
	Access the global tolerance to determine congruent spaces.

static void	SetGlobalDefaultDirectionTolerance (double)
	Access the global tolerance to determine congruent spaces.

static bool	SetGlobalDefaultThreader (const std::string &threader)
	Set/Get the default threader used for process objects.

static std::string	GetGlobalDefaultThreader ()
	Set/Get the default threader used for process objects.

static void	SetGlobalDefaultNumberOfThreads (unsigned int n)

static unsigned int	GetGlobalDefaultNumberOfThreads ()
	Set/Get the default threader used for process objects.

Static Protected Member Functions inherited from itk::simple::ProcessObject
template<class TImageType>
static TImageType::ConstPointer	CastImageToITK (const Image &img)

template<class TPixelType, unsigned int VImageDimension, unsigned int VLength, template< typename, unsigned int > class TVector>
static Image	CastITKToImage (itk::Image< TVector< TPixelType, VLength >, VImageDimension > *img)

template<unsigned int VImageDimension, unsigned int VLength, template< unsigned int > class TVector>
static Image	CastITKToImage (itk::Image< TVector< VLength >, VImageDimension > *img)

template<class TImageType>
static Image	CastITKToImage (TImageType *img)

static const itk::EventObject &	GetITKEventObject (EventEnum e)

template<typename T>
static std::ostream &	ToStringHelper (std::ostream &os, const T &v)

static std::ostream &	ToStringHelper (std::ostream &os, const char &v)

static std::ostream &	ToStringHelper (std::ostream &os, const signed char &v)

static std::ostream &	ToStringHelper (std::ostream &os, const unsigned char &v)

Member Typedef Documentation

◆ EvaluateMemberFunctionType

typedef double(ImageRegistrationMethod::* itk::simple::ImageRegistrationMethod::EvaluateMemberFunctionType) (const Image &fixed, const Image &moving)

private

Definition at line 833 of file sitkImageRegistrationMethod.h.

◆ MemberFunctionType

typedef Transform(ImageRegistrationMethod::* itk::simple::ImageRegistrationMethod::MemberFunctionType) (const Image &fixed, const Image &moving)

private

Definition at line 832 of file sitkImageRegistrationMethod.h.

◆ Self

using itk::simple::ImageRegistrationMethod::Self = ImageRegistrationMethod

Definition at line 90 of file sitkImageRegistrationMethod.h.

◆ Superclass

using itk::simple::ImageRegistrationMethod::Superclass = ProcessObject

Definition at line 91 of file sitkImageRegistrationMethod.h.

Member Enumeration Documentation

◆ EstimateLearningRateType

enum itk::simple::ImageRegistrationMethod::EstimateLearningRateType

Enumerator
Never	Never run estimation, use provided value.
Once	Estimate learning once each level, ignore provided values.
EachIteration	Estimate learning rate at each iteration.

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs.

Definition at line 291 of file sitkImageRegistrationMethod.h.

◆ MetricSamplingStrategyType

enum itk::simple::ImageRegistrationMethod::MetricSamplingStrategyType

Sampling strategies for obtaining points.

NONE: use all voxels, sampled points are the voxel centers.
REGULAR: sample every n-th voxel while traversing the image in scan-line order, then within each voxel randomly perturb from center.
RANDOM: sample image voxels with replacement using a uniform distribution, then within each voxel randomly perturb from center.

Enumerator
NONE
REGULAR
RANDOM

Examples: ImageRegistrationMethod4/ImageRegistrationMethod4.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs.

Definition at line 568 of file sitkImageRegistrationMethod.h.

◆ MetricType

enum itk::simple::ImageRegistrationMethod::MetricType

private

Enumerator
ANTSNeighborhoodCorrelation
Correlation
Demons
JointHistogramMutualInformation
MeanSquares
MattesMutualInformation

Definition at line 923 of file sitkImageRegistrationMethod.h.

◆ OptimizerScalesType

enum itk::simple::ImageRegistrationMethod::OptimizerScalesType

private

Enumerator
Manual
Jacobian
IndexShift
PhysicalShift

Definition at line 910 of file sitkImageRegistrationMethod.h.

◆ OptimizerType

enum itk::simple::ImageRegistrationMethod::OptimizerType

private

Enumerator
ConjugateGradientLineSearch
RegularStepGradientDescent
GradientDescent
GradientDescentLineSearch
LBFGSB
Exhaustive
Amoeba
Powell
OnePlusOneEvolutionary
LBFGS2

Definition at line 848 of file sitkImageRegistrationMethod.h.

Constructor & Destructor Documentation

◆ ~ImageRegistrationMethod()

itk::simple::ImageRegistrationMethod::~ImageRegistrationMethod ( )

override

◆ ImageRegistrationMethod()

itk::simple::ImageRegistrationMethod::ImageRegistrationMethod ( )

Referenced by detail::MemberFunctionAddressor< MemberFunctionType >.

Member Function Documentation

◆ AddITKObserver()

unsigned long itk::simple::ImageRegistrationMethod::AddITKObserver	(	const itk::EventObject &	,
		itk::Command *	)

overrideprotectedvirtual

Reimplemented from itk::simple::ProcessObject.

References AddITKObserver(), and itk::simple::ProcessObject::itk::simple::Command.

Referenced by AddITKObserver().

◆ CreateMetric()

template<class TImageType>

itk::ImageToImageMetricv4< TImageType, TImageType, TImageType, double, itk::DefaultImageToImageMetricTraitsv4< TImageType, TImageType, TImageType, double > > * itk::simple::ImageRegistrationMethod::CreateMetric ( )

protected

◆ CreateOptimizer()

itk::ObjectToObjectOptimizerBaseTemplate< double > * itk::simple::ImageRegistrationMethod::CreateOptimizer ( unsigned int numberOfTransformParameters )

protected

◆ CreateScalesEstimator()

template<typename TMetric>

itk::RegistrationParameterScalesEstimator< TMetric > * itk::simple::ImageRegistrationMethod::CreateScalesEstimator ( )

protected

◆ CreateSpatialObjectMask()

template<unsigned int VDimension>

itk::SpatialObject< VDimension > * itk::simple::ImageRegistrationMethod::CreateSpatialObjectMask ( const Image & mask )

protected

◆ CreateTransformParametersAdaptor()

template<typename TTransformAdaptorPointer, typename TRegistrationMethod>

std::vector< TTransformAdaptorPointer > itk::simple::ImageRegistrationMethod::CreateTransformParametersAdaptor ( TRegistrationMethod * method )

protected

◆ EvaluateInternal()

template<class TImage>

double itk::simple::ImageRegistrationMethod::EvaluateInternal	(	const Image &	fixed,
		const Image &	moving )

protected

Referenced by itk::simple::ImageRegistrationMethod::EvaluateMemberFunctionAddressor< TMemberFunctionPointer >::operator()().

◆ Execute()

Transform itk::simple::ImageRegistrationMethod::Execute	(	const Image &	fixed,
		const Image &	moving )

Optimize the configured registration problem.

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ ExecuteInternal()

template<class TImage>

Transform itk::simple::ImageRegistrationMethod::ExecuteInternal	(	const Image &	fixed,
		const Image &	moving )

protected

◆ GetCurrentLevel()

unsigned int itk::simple::ImageRegistrationMethod::GetCurrentLevel ( ) const

Examples: ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs.

◆ GetFixedInitialTransform()

Transform itk::simple::ImageRegistrationMethod::GetFixedInitialTransform ( ) const

inline

Set transform mapping to the fixed domain.

This transform is used to map from the virtual domain to the fixed image domain.

By default this transform is an identity, and not used.

See also: itk::ImageRegistrationMethodv4::SetFixedInitialTransform

Definition at line 224 of file sitkImageRegistrationMethod.h.

References m_FixedInitialTransform.

◆ GetInitialTransform()

Transform itk::simple::ImageRegistrationMethod::GetInitialTransform ( )

inline

Set the initial transform and parameters to optimize.

This transform is applied before the MovingInitialTransform, to map from the virtual image domain to the moving image domain.

If the inPlace flag is explicitly false, then the ITK registration will internally make a copy, and the transform will not be accessible during registration. Otherwise, the accessible InitialTransform value will be the same object used during registration, and will have a modified value upon completion.

See also: itk::ImageRegistrationMethodv4::SetInitialTransform

Definition at line 152 of file sitkImageRegistrationMethod.h.

References m_InitialTransform.

◆ GetInitialTransformInPlace()

bool itk::simple::ImageRegistrationMethod::GetInitialTransformInPlace ( ) const

inline

Set the initial transform and parameters to optimize.

This transform is applied before the MovingInitialTransform, to map from the virtual image domain to the moving image domain.

If the inPlace flag is explicitly false, then the ITK registration will internally make a copy, and the transform will not be accessible during registration. Otherwise, the accessible InitialTransform value will be the same object used during registration, and will have a modified value upon completion.

See also: itk::ImageRegistrationMethodv4::SetInitialTransform

Definition at line 157 of file sitkImageRegistrationMethod.h.

References m_InitialTransformInPlace.

◆ GetInterpolator()

InterpolatorEnum itk::simple::ImageRegistrationMethod::GetInterpolator ( )

inline

Set and get the interpolator to use.

Definition at line 117 of file sitkImageRegistrationMethod.h.

References m_Interpolator.

◆ GetMetricNumberOfValidPoints()

uint64_t itk::simple::ImageRegistrationMethod::GetMetricNumberOfValidPoints ( ) const

Current number of points used of metric evaluation

This is a active measurement connected to the registration processes during registration. This number is number of point in the virtual domain which overlap the fixed image and the moving image. It is valid for sparse or dense sampling. After execution of registration this will contain the last value.

◆ GetMetricSamplingPercentagePerLevel()

const std::vector< double > & itk::simple::ImageRegistrationMethod::GetMetricSamplingPercentagePerLevel ( ) const

Get the percentage of pixels used for metric evaluation.

◆ GetMetricValue()

double itk::simple::ImageRegistrationMethod::GetMetricValue ( ) const

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ GetMovingInitialTransform()

Transform itk::simple::ImageRegistrationMethod::GetMovingInitialTransform ( ) const

inline

Set a fixed transform component towards moving domain.

The InitialTransform is added to this transform to form the composite transform which maps from the virtual domain to the moving image's domain. The parameters of this transform are not optimized. This is an advanced option used when the output transformed with be composed into a composite transform.

By default this transform is an identity, and not used.

See also: itk::ImageRegistrationMethodv4::SetMovingInitialTransform

Definition at line 202 of file sitkImageRegistrationMethod.h.

References m_MovingInitialTransform.

◆ GetName()

std::string itk::simple::ImageRegistrationMethod::GetName ( ) const

inlineoverridevirtual

return user readable name for the filter

Implements itk::simple::ProcessObject.

Definition at line 98 of file sitkImageRegistrationMethod.h.

◆ GetOptimizerConvergenceValue()

double itk::simple::ImageRegistrationMethod::GetOptimizerConvergenceValue ( ) const

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs.

◆ GetOptimizerIteration()

unsigned int itk::simple::ImageRegistrationMethod::GetOptimizerIteration ( ) const

Active measurements which can be obtained during call backs.

This is a measurement. Its value is updated in the Execute methods, so the value will only be valid after an execution.

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ GetOptimizerLearningRate()

double itk::simple::ImageRegistrationMethod::GetOptimizerLearningRate ( ) const

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs.

◆ GetOptimizerPosition()

std::vector< double > itk::simple::ImageRegistrationMethod::GetOptimizerPosition ( ) const

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, and ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs.

◆ GetOptimizerScales()

std::vector< double > itk::simple::ImageRegistrationMethod::GetOptimizerScales ( ) const

Get the OptimizerScales.

If the scales are explicitly set then this method returns those values. If an estimator is used then this is an active measurement returning the scales estimated by the estimator and is only available during execution.

Examples: ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs.

◆ GetOptimizerStopConditionDescription()

std::string itk::simple::ImageRegistrationMethod::GetOptimizerStopConditionDescription ( ) const

Measurement updated at the end of execution.

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ GetOptimizerWeights()

std::vector< double > itk::simple::ImageRegistrationMethod::GetOptimizerWeights ( ) const

A per parameter weighting array for the optimizer.

Allows setting of a per-local-parameter weighting array. If unset, the weights are treated as identity. Weights are multiplied by the gradient at the same time scaling is applied. Weights are similar to the scales but not estimated, and may be used, for example, to easily mask out a particular parameter during optimization to hold it constant. Or they may be used to apply another kind of prior knowledge.

◆ MetricEvaluate()

double itk::simple::ImageRegistrationMethod::MetricEvaluate	(	const Image &	fixed,
		const Image &	moving )

Get the value of the metric given the state of the method.

Passing a fixed and moving image, this method constructs and configures a metric object to obtain the value. This will take into consideration the current transforms, metric, interpolator, and image masks. It does not take into consideration the sampling strategy, smoothing sigmas, or the shrink factors.

◆ MetricUseFixedImageGradientFilterOff()

void itk::simple::ImageRegistrationMethod::MetricUseFixedImageGradientFilterOff ( )

inline

Enable image gradient computation by a filter.

By default the image gradient is computed by itk::GradientRecursiveGaussianImageFiter. If disabled then a central difference function with be computed as needed.

See also: itk::ImageToImageMetricv4::SetUseFixedImageGradientFilter

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx.

Definition at line 599 of file sitkImageRegistrationMethod.h.

References SetMetricUseFixedImageGradientFilter().

◆ MetricUseFixedImageGradientFilterOn()

void itk::simple::ImageRegistrationMethod::MetricUseFixedImageGradientFilterOn ( )

inline

Enable image gradient computation by a filter.

By default the image gradient is computed by itk::GradientRecursiveGaussianImageFiter. If disabled then a central difference function with be computed as needed.

See also: itk::ImageToImageMetricv4::SetUseFixedImageGradientFilter

Definition at line 594 of file sitkImageRegistrationMethod.h.

References SetMetricUseFixedImageGradientFilter().

◆ MetricUseMovingImageGradientFilterOff()

void itk::simple::ImageRegistrationMethod::MetricUseMovingImageGradientFilterOff ( )

inline

Enable image gradient computation by a filter.

By default the image gradient is computed by itk::GradientRecursiveGaussianImageFiter. If disabled then a central difference function will be computed for each sample as needed.

See also: itk::ImageToImageMetricv4::SetUseMovingImageGradientFilter

Definition at line 624 of file sitkImageRegistrationMethod.h.

References SetMetricUseMovingImageGradientFilter().

◆ MetricUseMovingImageGradientFilterOn()

void itk::simple::ImageRegistrationMethod::MetricUseMovingImageGradientFilterOn ( )

inline

Enable image gradient computation by a filter.

By default the image gradient is computed by itk::GradientRecursiveGaussianImageFiter. If disabled then a central difference function will be computed for each sample as needed.

See also: itk::ImageToImageMetricv4::SetUseMovingImageGradientFilter

Definition at line 619 of file sitkImageRegistrationMethod.h.

References SetMetricUseMovingImageGradientFilter().

◆ OnActiveProcessDelete()

void itk::simple::ImageRegistrationMethod::OnActiveProcessDelete ( )

overrideprotectedvirtualnoexcept

Reimplemented from itk::simple::ProcessObject.

◆ PreUpdate()

void itk::simple::ImageRegistrationMethod::PreUpdate ( itk::ProcessObject * p )

overrideprotectedvirtual

Reimplemented from itk::simple::ProcessObject.

◆ RemoveITKObserver()

void itk::simple::ImageRegistrationMethod::RemoveITKObserver ( EventCommand & e )

overrideprotectedvirtual

Reimplemented from itk::simple::ProcessObject.

References RemoveITKObserver().

Referenced by RemoveITKObserver().

◆ SetFixedInitialTransform()

void itk::simple::ImageRegistrationMethod::SetFixedInitialTransform ( const Transform & transform )

inline

Set transform mapping to the fixed domain.

This transform is used to map from the virtual domain to the fixed image domain.

By default this transform is an identity, and not used.

See also: itk::ImageRegistrationMethodv4::SetFixedInitialTransform

Definition at line 219 of file sitkImageRegistrationMethod.h.

References m_FixedInitialTransform.

◆ SetInitialTransform() [1/2]

void itk::simple::ImageRegistrationMethod::SetInitialTransform ( const Transform & transform )

Set the initial transform and parameters to optimize.

This transform is applied before the MovingInitialTransform, to map from the virtual image domain to the moving image domain.

If the inPlace flag is explicitly false, then the ITK registration will internally make a copy, and the transform will not be accessible during registration. Otherwise, the accessible InitialTransform value will be the same object used during registration, and will have a modified value upon completion.

See also: itk::ImageRegistrationMethodv4::SetInitialTransform

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ SetInitialTransform() [2/2]

void itk::simple::ImageRegistrationMethod::SetInitialTransform	(	Transform &	transform,
		bool	inPlace = true )

Set the initial transform and parameters to optimize.

This transform is applied before the MovingInitialTransform, to map from the virtual image domain to the moving image domain.

If the inPlace flag is explicitly false, then the ITK registration will internally make a copy, and the transform will not be accessible during registration. Otherwise, the accessible InitialTransform value will be the same object used during registration, and will have a modified value upon completion.

See also: itk::ImageRegistrationMethodv4::SetInitialTransform

◆ SetInitialTransformAsBSpline()

void itk::simple::ImageRegistrationMethod::SetInitialTransformAsBSpline	(	BSplineTransform &	transform,
		bool	inPlace = true,
		const std::vector< unsigned int > &	scaleFactors = std::vector< unsigned int >() )

Set an initial BSpline transform to optimize.

A specialization of SetInitialTransform for BSplineTransforms which can take an additional scaleFactors parameter. The scaleFactors specifies the a isotropic scaling factor per level for the BSpline transform mesh size with respect to the initial transform. For example to double the BSpline mesh resolution at each of 3 levels the vector [1,2,4] should be provided.

If a per level scale factor is 0 or omitted than no transform adapter will be created for that level.

See also: itk::BSplineTransformParametersAdaptor

Examples: ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, and ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx.

◆ SetInterpolator()

void itk::simple::ImageRegistrationMethod::SetInterpolator ( InterpolatorEnum Interpolator )

inline

Set and get the interpolator to use.

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

Definition at line 122 of file sitkImageRegistrationMethod.h.

References m_Interpolator.

◆ SetMetricAsANTSNeighborhoodCorrelation()

void itk::simple::ImageRegistrationMethod::SetMetricAsANTSNeighborhoodCorrelation ( unsigned int radius )

Use normalized cross correlation using a small neighborhood for each voxel between two images, with speed optimizations for dense registration.

See also: itk::ANTSNeighborhoodCorrelationImageToImageMetricv4

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx.

◆ SetMetricAsCorrelation()

void itk::simple::ImageRegistrationMethod::SetMetricAsCorrelation ( )

Use negative normalized cross correlation image metric.

See also: itk::CorrelationImageToImageMetricv4

Examples: ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ SetMetricAsDemons()

void itk::simple::ImageRegistrationMethod::SetMetricAsDemons ( double intensityDifferenceThreshold = 0.001 )

Use demons image metric.

See also: itk::DemonsImageToImageMetricv4

◆ SetMetricAsJointHistogramMutualInformation()

void itk::simple::ImageRegistrationMethod::SetMetricAsJointHistogramMutualInformation	(	unsigned int	numberOfHistogramBins = 20,
		double	varianceForJointPDFSmoothing = 1.5 )

Use mutual information between two images.

See also: itk::JointHistogramMutualInformationImageToImageMetricv4

Examples: ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx.

◆ SetMetricAsMattesMutualInformation()

void itk::simple::ImageRegistrationMethod::SetMetricAsMattesMutualInformation ( unsigned int numberOfHistogramBins = 50 )

Use the mutual information between two images to be registered using the method of Mattes et al.

See also: itk::MattesMutualInformationImageToImageMetricv4

Examples: ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, and ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx.

◆ SetMetricAsMeanSquares()

void itk::simple::ImageRegistrationMethod::SetMetricAsMeanSquares ( )

Use negative means squares image metric.

See also: itk::MeanSquaresImageToImageMetricv4

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, and ImageRegistrationMethod1/ImageRegistrationMethod1.cxx.

◆ SetMetricFixedMask()

void itk::simple::ImageRegistrationMethod::SetMetricFixedMask ( const Image & binaryMask )

Set an image mask in order to restrict the sampled points for the metric.

The image is expected to be in the same physical space as the FixedImage, and if the pixel type is not UInt8 than the image will base cast.

See also: itk::ImageToImageMetricv4::SetFixedImageMask

◆ SetMetricMovingMask()

void itk::simple::ImageRegistrationMethod::SetMetricMovingMask ( const Image & binaryMask )

Set an image mask in order to restrict the sampled points for the metric in the moving image space.

The image is expected to be in the same physical space as the MovingImage, and if the pixel type is not UInt8 than the image will base cast.

See also: itk::ImageToImageMetricv4::SetMovingImageMask

◆ SetMetricSamplingPercentage()

void itk::simple::ImageRegistrationMethod::SetMetricSamplingPercentage	(	double	percentage,
		unsigned int	seed = sitkWallClock )

Set percentage of pixels sampled for metric evaluation.

The percentage is of the number of pixels in the virtual domain per level after the shrink factor has been applied.

The seed parameter is used to seed the pseudo-random number generator used in generating the sampling set of points. If the seed parameter is 0, then the wall clock is used, otherwise the fixed seed is used for reproducible behavior.

When providing multiple values the number of entries must match the number of shrink factors and smoothing sigmas. If a single value is given, then the same percentage is used for all levels.

See also: itk::ImageRegistrationMethodv4::SetMetricSamplingPercentage

Examples: ImageRegistrationMethod4/ImageRegistrationMethod4.cs, and ImageRegistrationMethod4/ImageRegistrationMethod4.cxx.

References itk::simple::sitkWallClock.

◆ SetMetricSamplingPercentagePerLevel()

void itk::simple::ImageRegistrationMethod::SetMetricSamplingPercentagePerLevel	(	const std::vector< double > &	percentage,
		unsigned int	seed = sitkWallClock )

Set percentage of pixels sampled for metric evaluation.

The percentage is of the number of pixels in the virtual domain per level after the shrink factor has been applied.

The seed parameter is used to seed the pseudo-random number generator used in generating the sampling set of points. If the seed parameter is 0, then the wall clock is used, otherwise the fixed seed is used for reproducible behavior.

When providing multiple values the number of entries must match the number of shrink factors and smoothing sigmas. If a single value is given, then the same percentage is used for all levels.

See also: itk::ImageRegistrationMethodv4::SetMetricSamplingPercentage

References itk::simple::sitkWallClock.

◆ SetMetricSamplingStrategy()

void itk::simple::ImageRegistrationMethod::SetMetricSamplingStrategy ( MetricSamplingStrategyType strategy )

Set sampling strategy for sample generation.

See also: itk::ImageRegistrationMethodv4::SetMetricSamplingStrategy

Examples: ImageRegistrationMethod4/ImageRegistrationMethod4.cs, ImageRegistrationMethod4/ImageRegistrationMethod4.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ SetMetricUseFixedImageGradientFilter()

void itk::simple::ImageRegistrationMethod::SetMetricUseFixedImageGradientFilter ( bool )

Enable image gradient computation by a filter.

By default the image gradient is computed by itk::GradientRecursiveGaussianImageFiter. If disabled then a central difference function with be computed as needed.

See also: itk::ImageToImageMetricv4::SetUseFixedImageGradientFilter

Referenced by MetricUseFixedImageGradientFilterOff(), and MetricUseFixedImageGradientFilterOn().

◆ SetMetricUseMovingImageGradientFilter()

void itk::simple::ImageRegistrationMethod::SetMetricUseMovingImageGradientFilter ( bool )

Enable image gradient computation by a filter.

By default the image gradient is computed by itk::GradientRecursiveGaussianImageFiter. If disabled then a central difference function will be computed for each sample as needed.

See also: itk::ImageToImageMetricv4::SetUseMovingImageGradientFilter

Referenced by MetricUseMovingImageGradientFilterOff(), and MetricUseMovingImageGradientFilterOn().

◆ SetMovingInitialTransform()

void itk::simple::ImageRegistrationMethod::SetMovingInitialTransform ( const Transform & transform )

inline

Set a fixed transform component towards moving domain.

The InitialTransform is added to this transform to form the composite transform which maps from the virtual domain to the moving image's domain. The parameters of this transform are not optimized. This is an advanced option used when the output transformed with be composed into a composite transform.

By default this transform is an identity, and not used.

See also: itk::ImageRegistrationMethodv4::SetMovingInitialTransform

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx.

Definition at line 197 of file sitkImageRegistrationMethod.h.

References m_MovingInitialTransform.

◆ SetOptimizerAsAmoeba()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsAmoeba	(	double	simplexDelta,
		unsigned int	numberOfIterations,
		double	parametersConvergenceTolerance = 1e-8,
		double	functionConvergenceTolerance = 1e-4,
		bool	withRestarts = false )

Set optimizer to Nelder-Mead downhill simplex algorithm.

The simplexDelta is the value that is added and subtracted to the initial parameters to create the initial simplex. The simplexDelta can be indirectly set per parameter by the optimizer parameter scaling either manually or by using an estimator.

See also: ImageRegistrationMethod::SetOptimizerScales; itk::AmoebaOptimizerv4

◆ SetOptimizerAsConjugateGradientLineSearch()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsConjugateGradientLineSearch	(	double	learningRate,
		unsigned int	numberOfIterations,
		double	convergenceMinimumValue = 1e-6,
		unsigned int	convergenceWindowSize = 10,
		double	lineSearchLowerLimit = 0,
		double	lineSearchUpperLimit = 5.0,
		double	lineSearchEpsilon = 0.01,
		unsigned int	lineSearchMaximumIterations = 20,
		EstimateLearningRateType	estimateLearningRate = Once,
		double	maximumStepSizeInPhysicalUnits = 0.0 )

Conjugate gradient descent optimizer with a golden section line search for nonlinear optimization.

See also: itk::ConjugateGradientLineSearchOptimizerv4Template

References Once.

◆ SetOptimizerAsExhaustive()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsExhaustive	(	const std::vector< unsigned int > &	numberOfSteps,
		double	stepLength = 1.0 )

Set the optimizer to sample the metric at regular steps.

At each iteration the GetOptimizerIteration, can be used to index into the sampling grid along with the GetCurrentMetricValue.

The resulting transform and value at the end of execution is the best location.

The OptimizerScales can be used to perform anisotropic sampling.

Note: This optimizer is not suitable for use in conjunction with the multiple scales.

See also: itk::ExhaustiveOptimizerv4

Examples: ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, and ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx.

◆ SetOptimizerAsGradientDescent()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsGradientDescent	(	double	learningRate,
		unsigned int	numberOfIterations,
		double	convergenceMinimumValue = 1e-6,
		unsigned int	convergenceWindowSize = 10,
		EstimateLearningRateType	estimateLearningRate = Once,
		double	maximumStepSizeInPhysicalUnits = 0.0 )

Gradient descent optimizer.

See also: itk::GradientDescentOptimizerv4Template

Examples: ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

References Once.

◆ SetOptimizerAsGradientDescentLineSearch()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsGradientDescentLineSearch	(	double	learningRate,
		unsigned int	numberOfIterations,
		double	convergenceMinimumValue = 1e-6,
		unsigned int	convergenceWindowSize = 10,
		double	lineSearchLowerLimit = 0,
		double	lineSearchUpperLimit = 5.0,
		double	lineSearchEpsilon = 0.01,
		unsigned int	lineSearchMaximumIterations = 20,
		EstimateLearningRateType	estimateLearningRate = Once,
		double	maximumStepSizeInPhysicalUnits = 0.0 )

Gradient descent optimizer with a golden section line search.

See also: itk::GradientDescentLineSearchOptimizerv4Template

Examples: ImageRegistrationMethod2/ImageRegistrationMethod2.cs, ImageRegistrationMethod2/ImageRegistrationMethod2.cxx, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, and ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx.

References Once.

◆ SetOptimizerAsLBFGS2()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsLBFGS2	(	double	solutionAccuracy = 1e-5,
		unsigned int	numberOfIterations = 0,
		unsigned int	hessianApproximateAccuracy = 6,
		unsigned int	deltaConvergenceDistance = 0,
		double	deltaConvergenceTolerance = 1e-5,
		unsigned int	lineSearchMaximumEvaluations = 40,
		double	lineSearchMinimumStep = 1e-20,
		double	lineSearchMaximumStep = 1e20,
		double	lineSearchAccuracy = 1e-4 )

Limited memory Broyden Fletcher Goldfarb Shannon minimization without bounds.

The default parameters utilize LBFGSB in unbounded mode. This version is from LibLBFGS.

There are upto 3 stopping criteria:

the solution accuracy which is the magnitude of the gradient
the delta convergence which ensures the decrease of the metric
maximum number of iterations

See also: itk::LBFGS2Optimizerv4

◆ SetOptimizerAsLBFGSB()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsLBFGSB	(	double	gradientConvergenceTolerance = 1e-5,
		unsigned int	numberOfIterations = 500,
		unsigned int	maximumNumberOfCorrections = 5,
		unsigned int	maximumNumberOfFunctionEvaluations = 2000,
		double	costFunctionConvergenceFactor = 1e+7,
		double	lowerBound = std::numeric_limits< double >::min(),
		double	upperBound = std::numeric_limits< double >::max(),
		bool	trace = false )

Limited memory Broyden Fletcher Goldfarb Shannon minimization with simple bounds.

The default parameters utilize LBFGSB in unbounded mode.

See also: itk::LBFGSBOptimizerv4

Examples: ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cs, and ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.cxx.

◆ SetOptimizerAsOnePlusOneEvolutionary()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsOnePlusOneEvolutionary	(	unsigned int	numberOfIterations = 100,
		double	epsilon = 1.5e-4,
		double	initialRadius = 1.01,
		double	growthFactor = -1.0,
		double	shrinkFactor = -1.0,
		unsigned int	seed = sitkWallClock )

1+1 evolutionary optimizer strategy.

The seed parameter is used to seed the pseudo-random number generator. If the seed parameter is 0, then the wall clock is used to seed, otherwise the fixed seed is used for reproducible behavior.

See also: itk::OnePlusOneEvolutionaryOptimizerv4

References itk::simple::sitkWallClock.

◆ SetOptimizerAsPowell()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsPowell	(	unsigned int	numberOfIterations = 100,
		unsigned int	maximumLineIterations = 100,
		double	stepLength = 1,
		double	stepTolerance = 1e-6,
		double	valueTolerance = 1e-6 )

Powell optimization using Brent line search.

See also: itk::PowellOptimizerv4

◆ SetOptimizerAsRegularStepGradientDescent()

void itk::simple::ImageRegistrationMethod::SetOptimizerAsRegularStepGradientDescent	(	double	learningRate,
		double	minStep,
		unsigned int	numberOfIterations,
		double	relaxationFactor = 0.5,
		double	gradientMagnitudeTolerance = 1e-4,
		EstimateLearningRateType	estimateLearningRate = Never,
		double	maximumStepSizeInPhysicalUnits = 0.0 )

Regular Step Gradient descent optimizer.

See also: itk::RegularStepGradientDescentOptimizerv4

Examples: ImageRegistrationMethod1/ImageRegistrationMethod1.cs, ImageRegistrationMethod1/ImageRegistrationMethod1.cxx, ImageRegistrationMethod3/ImageRegistrationMethod3.cs, ImageRegistrationMethod3/ImageRegistrationMethod3.cxx, ImageRegistrationMethod4/ImageRegistrationMethod4.cs, and ImageRegistrationMethod4/ImageRegistrationMethod4.cxx.

References Never.

◆ SetOptimizerScales()

void itk::simple::ImageRegistrationMethod::SetOptimizerScales ( const std::vector< double > & scales )

Manually set per parameter weighting for the transform parameters.

Examples: ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cs, and ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.cxx.

◆ SetOptimizerScalesFromIndexShift()

void itk::simple::ImageRegistrationMethod::SetOptimizerScalesFromIndexShift	(	unsigned int	centralRegionRadius = 5,
		double	smallParameterVariation = 0.01 )

Estimate scales from maximum voxel shift in index space cause by parameter change.

See also: itk::RegistrationParameterScalesFromIndexShift

Examples: ImageRegistrationMethod3/ImageRegistrationMethod3.cs, and ImageRegistrationMethod3/ImageRegistrationMethod3.cxx.

◆ SetOptimizerScalesFromJacobian()

void itk::simple::ImageRegistrationMethod::SetOptimizerScalesFromJacobian ( unsigned int centralRegionRadius = 5 )

Estimate scales from Jacobian norms.

This scales estimator works well with versor based transforms.

See also: itk::RegistrationParameterScalesFromJacobian

◆ SetOptimizerScalesFromPhysicalShift()

void itk::simple::ImageRegistrationMethod::SetOptimizerScalesFromPhysicalShift	(	unsigned int	centralRegionRadius = 5,
		double	smallParameterVariation = 0.01 )

Estimating scales of transform parameters a step sizes, from the maximum voxel shift in physical space caused by a parameter change.

See also: itk::RegistrationParameterScalesFromPhysicalShift

Examples: ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx, ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ SetOptimizerWeights()

void itk::simple::ImageRegistrationMethod::SetOptimizerWeights ( const std::vector< double > & weights )

A per parameter weighting array for the optimizer.

Allows setting of a per-local-parameter weighting array. If unset, the weights are treated as identity. Weights are multiplied by the gradient at the same time scaling is applied. Weights are similar to the scales but not estimated, and may be used, for example, to easily mask out a particular parameter during optimization to hold it constant. Or they may be used to apply another kind of prior knowledge.

Examples: ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cs, and ImageRegistrationOptimizerWeights/ImageRegistrationOptimizerWeights.cxx.

◆ SetShrinkFactorsPerLevel()

void itk::simple::ImageRegistrationMethod::SetShrinkFactorsPerLevel ( const std::vector< unsigned int > & shrinkFactors )

Set the isotropic shrink factors for each level.

The virtual domain image is shrunk by this factor relative to the full size of the original virtual domain.

See also: itk::ImageRegistrationMethodv4::SetShrinkFactorsPerLevel

Examples: ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx.

◆ SetSmoothingSigmasAreSpecifiedInPhysicalUnits()

void itk::simple::ImageRegistrationMethod::SetSmoothingSigmasAreSpecifiedInPhysicalUnits ( bool arg )

Enable the smoothing sigmas for each level in physical units (default) or in terms of voxels.

See also: itk::ImageRegistrationMethodv4::SetSmoothingSigmasAreSpecifiedInPhysicalUnits

Referenced by SmoothingSigmasAreSpecifiedInPhysicalUnitsOff(), and SmoothingSigmasAreSpecifiedInPhysicalUnitsOn().

◆ SetSmoothingSigmasPerLevel()

void itk::simple::ImageRegistrationMethod::SetSmoothingSigmasPerLevel ( const std::vector< double > & smoothingSigmas )

Set the sigmas of Gaussian used for smoothing.

The smoothing is applied to both the fixed and the moving images at each level. The number of smoothing sigmas must match the number of shrink factors.

See also: itk::ImageRegistrationMethodv4::SetSmoothingSigmasPerLevel

Examples: ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cs, ImageRegistrationMethodBSpline2/ImageRegistrationMethodBSpline2.cxx, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cs, ImageRegistrationMethodBSpline3/ImageRegistrationMethodBSpline3.cxx, ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cs, and ImageRegistrationMethodDisplacement1/ImageRegistrationMethodDisplacement1.cxx.

◆ SetupMetric()

template<class TImageType>

void itk::simple::ImageRegistrationMethod::SetupMetric	(	itk::ImageToImageMetricv4< TImageType, TImageType, TImageType, double, itk::DefaultImageToImageMetricTraitsv4< TImageType, TImageType, TImageType, double > > *	,
		const TImageType *	,
		const TImageType *	)

protected

◆ SetVirtualDomain()

void itk::simple::ImageRegistrationMethod::SetVirtualDomain	(	const std::vector< uint32_t > &	virtualSize,
		const std::vector< double > &	virtualOrigin,
		const std::vector< double > &	virtualSpacing,
		const std::vector< double > &	virtualDirection )

Set the virtual domain used for sampling.

◆ SetVirtualDomainFromImage()

void itk::simple::ImageRegistrationMethod::SetVirtualDomainFromImage ( const Image & virtualImage )

Set the virtual domain used for sampling.

◆ SmoothingSigmasAreSpecifiedInPhysicalUnitsOff()

void itk::simple::ImageRegistrationMethod::SmoothingSigmasAreSpecifiedInPhysicalUnitsOff ( )

inline

Enable the smoothing sigmas for each level in physical units (default) or in terms of voxels.

See also: itk::ImageRegistrationMethodv4::SetSmoothingSigmasAreSpecifiedInPhysicalUnits

Definition at line 666 of file sitkImageRegistrationMethod.h.

References SetSmoothingSigmasAreSpecifiedInPhysicalUnits().

◆ SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()

void itk::simple::ImageRegistrationMethod::SmoothingSigmasAreSpecifiedInPhysicalUnitsOn ( )

inline

Enable the smoothing sigmas for each level in physical units (default) or in terms of voxels.

See also: itk::ImageRegistrationMethodv4::SetSmoothingSigmasAreSpecifiedInPhysicalUnits

Definition at line 661 of file sitkImageRegistrationMethod.h.

References SetSmoothingSigmasAreSpecifiedInPhysicalUnits().

◆ StopRegistration()

bool itk::simple::ImageRegistrationMethod::StopRegistration ( )

Stop Registration if actively running.

This is an active method which can be called during a callback. Invoking this method will halt the registration at the current iteration, if supported by the optimizer. The LBFGSB, LBFGS2, and the Amoeba optimizers do not support user stopping.

If user stopping is not supported or the optimizer is available ( not executed ), then false will be returned.

◆ ToString()

std::string itk::simple::ImageRegistrationMethod::ToString ( ) const

overridevirtual

Print the information about the object to a string.

If called when the process is being executed ( during a callback ), the ITK Optimizer and Transform objects will be printed.

Reimplemented from itk::simple::ProcessObject.

Friends And Related Symbol Documentation

◆ detail::MemberFunctionAddressor< MemberFunctionType >

friend struct detail::MemberFunctionAddressor< MemberFunctionType >

friend

Definition at line 833 of file sitkImageRegistrationMethod.h.

References ImageRegistrationMethod().

Member Data Documentation

◆ m_ActiveOptimizer

itk::ObjectToObjectOptimizerBaseTemplate<double>* itk::simple::ImageRegistrationMethod::m_ActiveOptimizer

private

Definition at line 957 of file sitkImageRegistrationMethod.h.

◆ m_FixedInitialTransform

Transform itk::simple::ImageRegistrationMethod::m_FixedInitialTransform

private

Definition at line 840 of file sitkImageRegistrationMethod.h.

Referenced by GetFixedInitialTransform(), and SetFixedInitialTransform().

◆ m_InitialTransform

Transform itk::simple::ImageRegistrationMethod::m_InitialTransform

private

Definition at line 837 of file sitkImageRegistrationMethod.h.

Referenced by GetInitialTransform().

◆ m_InitialTransformInPlace

bool itk::simple::ImageRegistrationMethod::m_InitialTransformInPlace

private

Definition at line 838 of file sitkImageRegistrationMethod.h.

Referenced by GetInitialTransformInPlace().

◆ m_Interpolator

InterpolatorEnum itk::simple::ImageRegistrationMethod::m_Interpolator

private

Definition at line 836 of file sitkImageRegistrationMethod.h.

Referenced by GetInterpolator(), and SetInterpolator().

◆ m_Iteration

unsigned int itk::simple::ImageRegistrationMethod::m_Iteration

private

Definition at line 954 of file sitkImageRegistrationMethod.h.

◆ m_MetricFixedMaskImage

Image itk::simple::ImageRegistrationMethod::m_MetricFixedMaskImage

private

Definition at line 938 of file sitkImageRegistrationMethod.h.

◆ m_MetricIntensityDifferenceThreshold

double itk::simple::ImageRegistrationMethod::m_MetricIntensityDifferenceThreshold

private

Definition at line 934 of file sitkImageRegistrationMethod.h.

◆ m_MetricMovingMaskImage

Image itk::simple::ImageRegistrationMethod::m_MetricMovingMaskImage

private

Definition at line 939 of file sitkImageRegistrationMethod.h.

◆ m_MetricNumberOfHistogramBins

unsigned int itk::simple::ImageRegistrationMethod::m_MetricNumberOfHistogramBins

private

Definition at line 935 of file sitkImageRegistrationMethod.h.

◆ m_MetricRadius

unsigned int itk::simple::ImageRegistrationMethod::m_MetricRadius

private

Definition at line 933 of file sitkImageRegistrationMethod.h.

◆ m_MetricSamplingPercentage

std::vector<double> itk::simple::ImageRegistrationMethod::m_MetricSamplingPercentage

private

Definition at line 941 of file sitkImageRegistrationMethod.h.

◆ m_MetricSamplingSeed

unsigned int itk::simple::ImageRegistrationMethod::m_MetricSamplingSeed

private

Definition at line 943 of file sitkImageRegistrationMethod.h.

◆ m_MetricSamplingStrategy

MetricSamplingStrategyType itk::simple::ImageRegistrationMethod::m_MetricSamplingStrategy

private

Definition at line 942 of file sitkImageRegistrationMethod.h.

◆ m_MetricType

MetricType itk::simple::ImageRegistrationMethod::m_MetricType

private

Definition at line 932 of file sitkImageRegistrationMethod.h.

◆ m_MetricUseFixedImageGradientFilter

bool itk::simple::ImageRegistrationMethod::m_MetricUseFixedImageGradientFilter

private

Definition at line 945 of file sitkImageRegistrationMethod.h.

◆ m_MetricUseMovingImageGradientFilter

bool itk::simple::ImageRegistrationMethod::m_MetricUseMovingImageGradientFilter

private

Definition at line 946 of file sitkImageRegistrationMethod.h.

◆ m_MetricValue

double itk::simple::ImageRegistrationMethod::m_MetricValue

private

Definition at line 953 of file sitkImageRegistrationMethod.h.

◆ m_MetricVarianceForJointPDFSmoothing

double itk::simple::ImageRegistrationMethod::m_MetricVarianceForJointPDFSmoothing

private

Definition at line 936 of file sitkImageRegistrationMethod.h.

◆ m_MovingInitialTransform

Transform itk::simple::ImageRegistrationMethod::m_MovingInitialTransform

private

Definition at line 839 of file sitkImageRegistrationMethod.h.

Referenced by GetMovingInitialTransform(), and SetMovingInitialTransform().

◆ m_NumberOfValidPoints

uint64_t itk::simple::ImageRegistrationMethod::m_NumberOfValidPoints

private

Definition at line 955 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerConvergenceMinimumValue

double itk::simple::ImageRegistrationMethod::m_OptimizerConvergenceMinimumValue

private

Definition at line 873 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerConvergenceWindowSize

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerConvergenceWindowSize

private

Definition at line 874 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerCostFunctionConvergenceFactor

double itk::simple::ImageRegistrationMethod::m_OptimizerCostFunctionConvergenceFactor

private

Definition at line 878 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerDeltaConvergenceDistance

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerDeltaConvergenceDistance

private

Definition at line 898 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerDeltaConvergenceTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerDeltaConvergenceTolerance

private

Definition at line 899 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerEpsilon

double itk::simple::ImageRegistrationMethod::m_OptimizerEpsilon

private

Definition at line 891 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerEstimateLearningRate

EstimateLearningRateType itk::simple::ImageRegistrationMethod::m_OptimizerEstimateLearningRate

private

Definition at line 869 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerFunctionConvergenceTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerFunctionConvergenceTolerance

private

Definition at line 886 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerGradientConvergenceTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerGradientConvergenceTolerance

private

Definition at line 875 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerGradientMagnitudeTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerGradientMagnitudeTolerance

private

Definition at line 872 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerGrowthFactor

double itk::simple::ImageRegistrationMethod::m_OptimizerGrowthFactor

private

Definition at line 893 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerHessianApproximationAccuracy

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerHessianApproximationAccuracy

private

Definition at line 897 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerInitialRadius

double itk::simple::ImageRegistrationMethod::m_OptimizerInitialRadius

private

Definition at line 892 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLearningRate

double itk::simple::ImageRegistrationMethod::m_OptimizerLearningRate

private

Definition at line 862 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchAccuracy

double itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchAccuracy

private

Definition at line 903 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchEpsilon

double itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchEpsilon

private

Definition at line 867 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchLowerLimit

double itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchLowerLimit

private

Definition at line 865 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchMaximumEvaluations

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchMaximumEvaluations

private

Definition at line 900 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchMaximumIterations

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchMaximumIterations

private

Definition at line 868 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchMaximumStep

double itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchMaximumStep

private

Definition at line 902 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchMinimumStep

double itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchMinimumStep

private

Definition at line 901 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLineSearchUpperLimit

double itk::simple::ImageRegistrationMethod::m_OptimizerLineSearchUpperLimit

private

Definition at line 866 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerLowerBound

double itk::simple::ImageRegistrationMethod::m_OptimizerLowerBound

private

Definition at line 879 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerMaximumLineIterations

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerMaximumLineIterations

private

Definition at line 888 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerMaximumNumberOfCorrections

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerMaximumNumberOfCorrections

private

Definition at line 876 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerMaximumNumberOfFunctionEvaluations

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerMaximumNumberOfFunctionEvaluations

private

Definition at line 877 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerMaximumStepSizeInPhysicalUnits

double itk::simple::ImageRegistrationMethod::m_OptimizerMaximumStepSizeInPhysicalUnits

private

Definition at line 870 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerMinimumStepLength

double itk::simple::ImageRegistrationMethod::m_OptimizerMinimumStepLength

private

Definition at line 863 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerNumberOfIterations

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerNumberOfIterations

private

Definition at line 864 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerNumberOfSteps

std::vector<unsigned int> itk::simple::ImageRegistrationMethod::m_OptimizerNumberOfSteps

private

Definition at line 882 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerParametersConvergenceTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerParametersConvergenceTolerance

private

Definition at line 885 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerRelaxationFactor

double itk::simple::ImageRegistrationMethod::m_OptimizerRelaxationFactor

private

Definition at line 871 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerScales

std::vector<double> itk::simple::ImageRegistrationMethod::m_OptimizerScales

private

Definition at line 918 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerScalesCentralRegionRadius

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerScalesCentralRegionRadius

private

Definition at line 919 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerScalesSmallParameterVariation

double itk::simple::ImageRegistrationMethod::m_OptimizerScalesSmallParameterVariation

private

Definition at line 920 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerScalesType

OptimizerScalesType itk::simple::ImageRegistrationMethod::m_OptimizerScalesType

private

Definition at line 917 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerSeed

unsigned int itk::simple::ImageRegistrationMethod::m_OptimizerSeed

private

Definition at line 895 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerShrinkFactor

double itk::simple::ImageRegistrationMethod::m_OptimizerShrinkFactor

private

Definition at line 894 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerSimplexDelta

double itk::simple::ImageRegistrationMethod::m_OptimizerSimplexDelta

private

Definition at line 884 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerSolutionAccuracy

double itk::simple::ImageRegistrationMethod::m_OptimizerSolutionAccuracy

private

Definition at line 896 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerStepLength

double itk::simple::ImageRegistrationMethod::m_OptimizerStepLength

private

Definition at line 883 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerStepTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerStepTolerance

private

Definition at line 889 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerTrace

bool itk::simple::ImageRegistrationMethod::m_OptimizerTrace

private

Definition at line 881 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerType

OptimizerType itk::simple::ImageRegistrationMethod::m_OptimizerType

private

Definition at line 861 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerUpperBound

double itk::simple::ImageRegistrationMethod::m_OptimizerUpperBound

private

Definition at line 880 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerValueTolerance

double itk::simple::ImageRegistrationMethod::m_OptimizerValueTolerance

private

Definition at line 890 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerWeights

std::vector<double> itk::simple::ImageRegistrationMethod::m_OptimizerWeights

private

Definition at line 908 of file sitkImageRegistrationMethod.h.

◆ m_OptimizerWithRestarts

bool itk::simple::ImageRegistrationMethod::m_OptimizerWithRestarts

private

Definition at line 887 of file sitkImageRegistrationMethod.h.

◆ m_pfGetCurrentLevel

std::function<unsigned int()> itk::simple::ImageRegistrationMethod::m_pfGetCurrentLevel

private

Definition at line 815 of file sitkImageRegistrationMethod.h.

◆ m_pfGetMetricNumberOfValidPoints

std::function<uint64_t()> itk::simple::ImageRegistrationMethod::m_pfGetMetricNumberOfValidPoints

private

Definition at line 809 of file sitkImageRegistrationMethod.h.

◆ m_pfGetMetricValue

std::function<double()> itk::simple::ImageRegistrationMethod::m_pfGetMetricValue

private

Definition at line 808 of file sitkImageRegistrationMethod.h.

◆ m_pfGetOptimizerConvergenceValue

std::function<double()> itk::simple::ImageRegistrationMethod::m_pfGetOptimizerConvergenceValue

private

Definition at line 807 of file sitkImageRegistrationMethod.h.

◆ m_pfGetOptimizerIteration

std::function<unsigned int()> itk::simple::ImageRegistrationMethod::m_pfGetOptimizerIteration

private

Definition at line 804 of file sitkImageRegistrationMethod.h.

◆ m_pfGetOptimizerLearningRate

std::function<double()> itk::simple::ImageRegistrationMethod::m_pfGetOptimizerLearningRate

private

Definition at line 806 of file sitkImageRegistrationMethod.h.

◆ m_pfGetOptimizerPosition

std::function<std::vector<double>()> itk::simple::ImageRegistrationMethod::m_pfGetOptimizerPosition

private

Definition at line 805 of file sitkImageRegistrationMethod.h.

◆ m_pfGetOptimizerScales

std::function<std::vector<double>()> itk::simple::ImageRegistrationMethod::m_pfGetOptimizerScales

private

Definition at line 810 of file sitkImageRegistrationMethod.h.

◆ m_pfGetOptimizerStopConditionDescription

std::function<std::string()> itk::simple::ImageRegistrationMethod::m_pfGetOptimizerStopConditionDescription

private

Definition at line 811 of file sitkImageRegistrationMethod.h.

◆ m_pfOptimizerStopRegistration

std::function<void()> itk::simple::ImageRegistrationMethod::m_pfOptimizerStopRegistration

private

Definition at line 812 of file sitkImageRegistrationMethod.h.

◆ m_pfUpdateWithBestValue

std::function<void(itk::TransformBase * outTransform)> itk::simple::ImageRegistrationMethod::m_pfUpdateWithBestValue

private

Definition at line 817 of file sitkImageRegistrationMethod.h.

◆ m_ShrinkFactorsPerLevel

std::vector<unsigned int> itk::simple::ImageRegistrationMethod::m_ShrinkFactorsPerLevel

private

Definition at line 948 of file sitkImageRegistrationMethod.h.

◆ m_SmoothingSigmasAreSpecifiedInPhysicalUnits

bool itk::simple::ImageRegistrationMethod::m_SmoothingSigmasAreSpecifiedInPhysicalUnits

private

Definition at line 950 of file sitkImageRegistrationMethod.h.

◆ m_SmoothingSigmasPerLevel

std::vector<double> itk::simple::ImageRegistrationMethod::m_SmoothingSigmasPerLevel

private

Definition at line 949 of file sitkImageRegistrationMethod.h.

◆ m_StopConditionDescription

std::string itk::simple::ImageRegistrationMethod::m_StopConditionDescription

private

Definition at line 952 of file sitkImageRegistrationMethod.h.

◆ m_TransformBSplineScaleFactors

std::vector<unsigned int> itk::simple::ImageRegistrationMethod::m_TransformBSplineScaleFactors

private

Definition at line 906 of file sitkImageRegistrationMethod.h.

◆ m_VirtualDomainDirection

std::vector<double> itk::simple::ImageRegistrationMethod::m_VirtualDomainDirection

private

Definition at line 845 of file sitkImageRegistrationMethod.h.

◆ m_VirtualDomainOrigin

std::vector<double> itk::simple::ImageRegistrationMethod::m_VirtualDomainOrigin

private

Definition at line 843 of file sitkImageRegistrationMethod.h.

◆ m_VirtualDomainSize

std::vector<uint32_t> itk::simple::ImageRegistrationMethod::m_VirtualDomainSize

private

Definition at line 842 of file sitkImageRegistrationMethod.h.

◆ m_VirtualDomainSpacing

std::vector<double> itk::simple::ImageRegistrationMethod::m_VirtualDomainSpacing

private

Definition at line 844 of file sitkImageRegistrationMethod.h.

The documentation for this class was generated from the following file:

sitkImageRegistrationMethod.h

Detailed Description

Classes

Public Types

Public Member Functions

Protected Member Functions

Private Types

Private Attributes

Friends

Additional Inherited Members

Member Typedef Documentation

◆ EvaluateMemberFunctionType

◆ MemberFunctionType

◆ Self

◆ Superclass

Member Enumeration Documentation

◆ EstimateLearningRateType

◆ MetricSamplingStrategyType

◆ MetricType

◆ OptimizerScalesType

◆ OptimizerType

Constructor & Destructor Documentation

◆ ~ImageRegistrationMethod()

◆ ImageRegistrationMethod()

Member Function Documentation

◆ AddITKObserver()

◆ CreateMetric()

◆ CreateOptimizer()

◆ CreateScalesEstimator()

◆ CreateSpatialObjectMask()

◆ CreateTransformParametersAdaptor()

◆ EvaluateInternal()

◆ Execute()

◆ ExecuteInternal()

◆ GetCurrentLevel()

◆ GetFixedInitialTransform()

◆ GetInitialTransform()

◆ GetInitialTransformInPlace()

◆ GetInterpolator()

◆ GetMetricNumberOfValidPoints()

◆ GetMetricSamplingPercentagePerLevel()

◆ GetMetricValue()

◆ GetMovingInitialTransform()

◆ GetName()

◆ GetOptimizerConvergenceValue()

◆ GetOptimizerIteration()

◆ GetOptimizerLearningRate()

◆ GetOptimizerPosition()

◆ GetOptimizerScales()

◆ GetOptimizerStopConditionDescription()

◆ GetOptimizerWeights()

◆ MetricEvaluate()

◆ MetricUseFixedImageGradientFilterOff()

◆ MetricUseFixedImageGradientFilterOn()

◆ MetricUseMovingImageGradientFilterOff()

◆ MetricUseMovingImageGradientFilterOn()

◆ OnActiveProcessDelete()

◆ PreUpdate()

◆ RemoveITKObserver()

◆ SetFixedInitialTransform()

◆ SetInitialTransform() [1/2]

◆ SetInitialTransform() [2/2]

◆ SetInitialTransformAsBSpline()

◆ SetInterpolator()

◆ SetMetricAsANTSNeighborhoodCorrelation()

◆ SetMetricAsCorrelation()

◆ SetMetricAsDemons()

◆ SetMetricAsJointHistogramMutualInformation()

◆ SetMetricAsMattesMutualInformation()

◆ SetMetricAsMeanSquares()

◆ SetMetricFixedMask()

◆ SetMetricMovingMask()

◆ SetMetricSamplingPercentage()

◆ SetMetricSamplingPercentagePerLevel()

◆ SetMetricSamplingStrategy()

◆ SetMetricUseFixedImageGradientFilter()

◆ SetMetricUseMovingImageGradientFilter()

◆ SetMovingInitialTransform()

◆ SetOptimizerAsAmoeba()

◆ SetOptimizerAsConjugateGradientLineSearch()

◆ SetOptimizerAsExhaustive()