#include <elxAdaptiveStochasticGradientDescent.h>

Detailed Description

template<class TElastix>
class elastix::AdaptiveStochasticGradientDescent< TElastix >

A gradient descent optimizer with an adaptive gain.

This class is a wrap around the AdaptiveStochasticGradientDescentOptimizer class. It takes care of setting parameters and printing progress information. For more information about the optimization method, please read the documentation of the AdaptiveStochasticGradientDescentOptimizer class.

This optimizer is very suitable to be used in combination with the Random image sampler, or with the RandomCoordinate image sampler, with the setting (NewSamplesEveryIteration "true"). Much effort has been spent on providing reasonable default values for all parameters, to simplify usage. In most registration problems, good results should be obtained without specifying any of the parameters described below (except the first of course, which defines the optimizer to use).

This optimization method is described in the following references:

[1] P. Cruz, "Almost sure convergence and asymptotical normality of a generalization of Kesten's stochastic approximation algorithm for multidimensional case." Technical Report, 2005. http://hdl.handle.net/2052/74

[2] S. Klein, J.P.W. Pluim, and M. Staring, M.A. Viergever, "Adaptive stochastic gradient descent optimization for image registration," International Journal of Computer Vision, vol. 81, no. 3, pp. 227-239, 2009. http://dx.doi.org/10.1007/s11263-008-0168-y

Acceleration in case of many transform parameters was proposed in the following paper:

[3] Y. Qiao, B. van Lew, B.P.F. Lelieveldt and M. Staring "Fast Automatic Step Size Estimation for Gradient Descent Optimization of Image Registration," IEEE Transactions on Medical Imaging, vol. 35, no. 2, pp. 391 - 403, February 2016. http://elastix.dev/marius/publications/2016_j_TMIa.php

The parameters used in this class are:

Parameters

Optimizer: Select this optimizer as follows:
(Optimizer "AdaptiveStochasticGradientDescent")

MaximumNumberOfIterations: The maximum number of iterations in each resolution.
example: (MaximumNumberOfIterations 100 100 50)
Default/recommended value: 500. When you are in a hurry, you may go down to 250 for example. When you have plenty of time, and want to be absolutely sure of the best results, a setting of 2000 is reasonable. In general, 500 gives satisfactory results.

MaximumNumberOfSamplingAttempts: The maximum number of sampling attempts. Sometimes not enough corresponding samples can be drawn, upon which an exception is thrown. With this parameter it is possible to try to draw another set of samples.
example: (MaximumNumberOfSamplingAttempts 10 15 10)
Default value: 0, i.e. just fail immediately, for backward compatibility.

AutomaticParameterEstimation: When this parameter is set to "true", many other parameters are calculated automatically: SP_a, SP_alpha, SigmoidMax, SigmoidMin, and SigmoidScale. In the elastix.log file the actually chosen values for these parameters can be found.
example: (AutomaticParameterEstimation "true")
Default/recommended value: "true". The parameter can be specified for each resolution, or for all resolutions at once.

UseAdaptiveStepSizes: When this parameter is set to "true", the adaptive step size mechanism described in the documentation of itk::AdaptiveStochasticGradientDescentOptimizer is used. The parameter can be specified for each resolution, or for all resolutions at once.
example: (UseAdaptiveStepSizes "true")
Default/recommend value: "true", because it makes the registration more robust. In case of using a RandomCoordinate sampler, with (UseRandomSampleRegion "true"), the adaptive step size mechanism is turned off, no matter the user setting.

MaximumStepLength: Also called $\delta$ . This parameter can be considered as the maximum voxel displacement between two iterations. The larger this parameter, the more aggressive the optimization. The parameter can be specified for each resolution, or for all resolutions at once.
example: (MaximumStepLength 1.0)
Default: mean voxel spacing of fixed and moving image. This seems to work well in general. This parameter only has influence when AutomaticParameterEstimation is used.

SP_a: The gain $a(k)$ at each iteration $k$ is defined by
$a(k) = SP\_a / (SP\_A + k + 1)^{SP\_alpha}$ .
SP_a can be defined for each resolution.
example: (SP_a 3200.0 3200.0 1600.0)
The default value is 400.0. Tuning this variable for you specific problem is recommended. Alternatively set the AutomaticParameterEstimation to "true". In that case, you do not need to specify SP_a. SP_a has no influence when AutomaticParameterEstimation is used.

SP_A: The gain $a(k)$ at each iteration $k$ is defined by
$a(k) = SP\_a / (SP\_A + k + 1)^{SP\_alpha}$ .
SP_A can be defined for each resolution.
example: (SP_A 50.0 50.0 100.0)
The default/recommended value for this particular optimizer is 20.0.

SP_alpha: The gain $a(k)$ at each iteration $k$ is defined by
$a(k) = SP\_a / (SP\_A + k + 1)^{SP\_alpha}$ .
SP_alpha can be defined for each resolution.
example: (SP_alpha 0.602 0.602 0.602)
The default/recommended value for this particular optimizer is 1.0. Alternatively set the AutomaticParameterEstimation to "true". In that case, you do not need to specify SP_alpha. SP_alpha has no influence when AutomaticParameterEstimation is used.

SigmoidMax: The maximum of the sigmoid function ( $f_{max}$ ). Must be larger than 0. The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidMax 1.0)
Default/recommended value: 1.0. This parameter has no influence when AutomaticParameterEstimation is used. In that case, always a value 1.0 is used.

SigmoidMin: The minimum of the sigmoid function ( $f_{min}$ ). Must be smaller than 0. The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidMin -0.8)
Default value: -0.8. This parameter has no influence when AutomaticParameterEstimation is used. In that case, the value is automatically determined, depending on the images, metric etc.

SigmoidScale: The scale/width of the sigmoid function ( $\omega$ ). The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidScale 0.00001)
Default value: 1e-8. This parameter has no influence when AutomaticParameterEstimation is used. In that case, the value is automatically determined, depending on the images, metric etc.

SigmoidInitialTime: the initial time input for the sigmoid ( $t_0$ ). Must be larger than 0.0. The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidInitialTime 0.0 5.0 5.0)
Default value: 0.0. When increased, the optimization starts with smaller steps, leaving the possibility to increase the steps when necessary. If set to 0.0, the method starts with with the largest step allowed.

NumberOfGradientMeasurements: Number of gradients N to estimate the average square magnitudes of the exact gradient and the approximation error. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NumberOfGradientMeasurements 10)
Default value: 0, which means that the value is automatically estimated. In principle, the more the better, but the slower. In practice N=10 is usually sufficient. But the automatic estimation achieved by N=0 also works good. The parameter has only influence when AutomaticParameterEstimation is used.

NumberOfJacobianMeasurements: The number of voxels M where the Jacobian is measured, which is used to estimate the covariance matrix. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NumberOfJacobianMeasurements 5000 10000 20000)
Default value: M = max( 1000, nrofparams ), with nrofparams the number of transform parameters. This is a rather crude rule of thumb, which seems to work in practice. In principle, the more the better, but the slower. The parameter has only influence when AutomaticParameterEstimation is used.

NumberOfSamplesForExactGradient: The number of image samples used to compute the 'exact' gradient. The samples are chosen on a uniform grid. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NumberOfSamplesForExactGradient 100000)
Default/recommended: 100000. This works in general. If the image is smaller, the number of samples is automatically reduced. In principle, the more the better, but the slower. The parameter has only influence when AutomaticParameterEstimation is used.

ASGDParameterEstimationMethod: The ASGD parameter estimation method used in this optimizer. The parameter can be specified for each resolution.
example: (ASGDParameterEstimationMethod "Original")
or (ASGDParameterEstimationMethod "DisplacementDistribution")
Default: Original.

MaximumDisplacementEstimationMethod: The suitable position selection method used only for displacement distribution estimation method. The parameter can be specified for each resolution.
example: (MaximumDisplacementEstimationMethod "2sigma")
or (MaximumDisplacementEstimationMethod "95percentile")
Default: 2sigma.

NoiseCompensation: Selects whether or not to use noise compensation. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NoiseCompensation "true")
Default/recommended: true.

Todo: : this class contains a lot of functional code, which actually does not belong here.

See also: AdaptiveStochasticGradientDescentOptimizer

Definition at line 193 of file elxAdaptiveStochasticGradientDescent.h.

Inheritance diagram for elastix::AdaptiveStochasticGradientDescent< TElastix >:

[legend]

Public Types
using	ConstPointer = itk::SmartPointer<const Self>

using	ITKBaseType = typename Superclass2::ITKBaseType

using	Pointer = itk::SmartPointer<Self>

using	Self = AdaptiveStochasticGradientDescent

using	SizeValueType = itk::SizeValueType

using	Superclass1 = AdaptiveStochasticGradientDescentOptimizer

using	Superclass2 = OptimizerBase<TElastix>

Public Types inherited from itk::AdaptiveStochasticGradientDescentOptimizer
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	Self = AdaptiveStochasticGradientDescentOptimizer

using	Superclass = StandardGradientDescentOptimizer

Public Types inherited from itk::StandardGradientDescentOptimizer
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	ScaledCostFunctionPointer

using	ScaledCostFunctionType

using	ScalesType

using	Self = StandardGradientDescentOptimizer

enum	StopConditionType

using	Superclass = GradientDescentOptimizer2

Public Types inherited from itk::GradientDescentOptimizer2
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	ScaledCostFunctionPointer

using	ScaledCostFunctionType

using	ScalesType

using	Self = GradientDescentOptimizer2

enum	StopConditionType { MaximumNumberOfIterations , MetricError , MinimumStepSize }

using	Superclass = ScaledSingleValuedNonLinearOptimizer

Public Types inherited from itk::ScaledSingleValuedNonLinearOptimizer
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	ScaledCostFunctionPointer = ScaledCostFunctionType::Pointer

using	ScaledCostFunctionType = ScaledSingleValuedCostFunction

using	ScalesType = NonLinearOptimizer::ScalesType

using	Self = ScaledSingleValuedNonLinearOptimizer

using	Superclass = SingleValuedNonLinearOptimizer

Public Types inherited from elastix::OptimizerBase< TElastix >
using	ElastixType

using	ITKBaseType = itk::Optimizer

using	ParametersType = typename ITKBaseType::ParametersType

using	RegistrationType

using	Self = OptimizerBase

using	Superclass = BaseComponentSE<TElastix>

Public Types inherited from elastix::BaseComponentSE< TElastix >
using	ElastixType = TElastix

using	RegistrationType = typename ElastixType::RegistrationBaseType

using	Self = BaseComponentSE

using	Superclass = BaseComponent

Public Member Functions
void	AfterEachIteration () override

void	AfterEachResolution () override

void	AfterRegistration () override

void	BeforeEachResolution () override

void	BeforeRegistration () override

	elxClassNameMacro ("AdaptiveStochasticGradientDescent")

virtual bool	GetAutomaticParameterEstimation () const

virtual const char *	GetClassName () const

virtual const SizeValueType &	GetMaximumNumberOfSamplingAttempts ()

virtual double	GetMaximumStepLength () const

	ITK_DISALLOW_COPY_AND_MOVE (AdaptiveStochasticGradientDescent)

void	MetricErrorResponse (itk::ExceptionObject &err) override

void	ResumeOptimization () override

virtual void	SetAutomaticParameterEstimation (bool _arg)

virtual void	SetMaximumNumberOfSamplingAttempts (SizeValueType _arg)

virtual void	SetMaximumStepLength (double _arg)

void	StartOptimization () override

Public Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
virtual double	GetSigmoidMax () const

virtual double	GetSigmoidMin () const

virtual double	GetSigmoidScale () const

virtual bool	GetUseAdaptiveStepSizes () const

	ITK_DISALLOW_COPY_AND_MOVE (AdaptiveStochasticGradientDescentOptimizer)

virtual void	SetSigmoidMax (double _arg)

virtual void	SetSigmoidMin (double _arg)

virtual void	SetSigmoidScale (double _arg)

virtual void	SetUseAdaptiveStepSizes (bool _arg)

Public Member Functions inherited from itk::StandardGradientDescentOptimizer
void	AdvanceOneStep () override

virtual double	GetCurrentTime () const

virtual double	GetInitialTime () const

virtual double	GetParam_A () const

virtual double	GetParam_a () const

virtual double	GetParam_alpha () const

	ITK_DISALLOW_COPY_AND_MOVE (StandardGradientDescentOptimizer)

virtual void	ResetCurrentTimeToInitialTime ()

virtual void	SetInitialTime (double _arg)

virtual void	SetParam_A (double _arg)

virtual void	SetParam_a (double _arg)

virtual void	SetParam_alpha (double _arg)

void	StartOptimization () override

Public Member Functions inherited from itk::GradientDescentOptimizer2
virtual unsigned int	GetCurrentIteration () const

virtual const DerivativeType &	GetGradient ()

virtual const double &	GetLearningRate ()

virtual const unsigned long &	GetNumberOfIterations ()

virtual const DerivativeType &	GetSearchDirection ()

virtual const StopConditionType &	GetStopCondition ()

virtual const double &	GetValue ()

	ITK_DISALLOW_COPY_AND_MOVE (GradientDescentOptimizer2)

virtual void	MetricErrorResponse (ExceptionObject &err)

virtual void	SetLearningRate (double _arg)

virtual void	SetNumberOfIterations (unsigned long _arg)

void	StartOptimization () override

virtual void	StopOptimization ()

Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
const ParametersType &	GetCurrentPosition () const override

virtual bool	GetMaximize () const

virtual const ScaledCostFunctionType *	GetScaledCostFunction ()

virtual const ParametersType &	GetScaledCurrentPosition ()

bool	GetUseScales () const

virtual void	InitializeScales ()

	ITK_DISALLOW_COPY_AND_MOVE (ScaledSingleValuedNonLinearOptimizer)

virtual void	MaximizeOff ()

virtual void	MaximizeOn ()

void	SetCostFunction (CostFunctionType *costFunction) override

virtual void	SetMaximize (bool _arg)

virtual void	SetUseScales (bool arg)

Public Member Functions inherited from elastix::OptimizerBase< TElastix >
void	AfterRegistrationBase () override

void	BeforeEachResolutionBase () override

ITKBaseType *	GetAsITKBaseType ()

const ITKBaseType *	GetAsITKBaseType () const

	ITK_DISALLOW_COPY_AND_MOVE (OptimizerBase)

virtual void	SetCurrentPositionPublic (const ParametersType &param)

virtual void	SetSinusScales (double amplitude, double frequency, unsigned long numberOfParameters)

Public Member Functions inherited from elastix::BaseComponentSE< TElastix >
void	AddTargetCellToIterationInfo (const char *const name)

const Configuration *	GetConfiguration () const

ElastixType *	GetElastix () const

auto &	GetIterationInfoAt (const char *const name)

RegistrationType *	GetRegistration () const

	ITK_DISALLOW_COPY_AND_MOVE (BaseComponentSE)

void	RemoveTargetCellFromIterationInfo (const char *const name)

void	SetConfiguration (const Configuration *_arg)

void	SetElastix (ElastixType *_arg)

Public Member Functions inherited from elastix::BaseComponent
virtual void	AfterEachIterationBase ()

virtual void	AfterEachResolutionBase ()

virtual int	BeforeAll ()

virtual int	BeforeAllBase ()

virtual void	BeforeRegistrationBase ()

virtual const char *	elxGetClassName () const

const char *	GetComponentLabel () const

	ITK_DISALLOW_COPY_AND_MOVE (BaseComponent)

	itkTypeMacroNoParent (BaseComponent)

void	SetComponentLabel (const char *label, unsigned int idx)

Static Public Member Functions
static Pointer	New ()

Static Public Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::StandardGradientDescentOptimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::GradientDescentOptimizer2
static Pointer	New ()

Static Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
static Pointer	New ()

Static Public Member Functions inherited from elastix::BaseComponent
template<typename TBaseComponent >
static auto	AsITKBaseType (TBaseComponent *const baseComponent) -> decltype(baseComponent->GetAsITKBaseType())

static void	InitializeElastixExecutable ()

static bool	IsElastixLibrary ()

Protected Types
using	AdvancedTransformPointer = typename AdvancedTransformType::Pointer

using	AdvancedTransformType

using	ComputeDisplacementDistributionType = itk::ComputeDisplacementDistribution<FixedImageType, TransformType>

using	ComputeJacobianTermsType = itk::ComputeJacobianTerms<FixedImageType, TransformType>

using	CoordinateRepresentationType = typename TransformType::ScalarType

using	FixedImageIndexType = typename FixedImageType::IndexType

using	FixedImagePointType = typename FixedImageType::PointType

using	FixedImageRegionType = typename FixedImageType::RegionType

using	FixedImageType = typename RegistrationType::FixedImageType

using	ImageGridSamplerPointer = typename ImageGridSamplerType::Pointer

using	ImageGridSamplerType = itk::ImageGridSampler<FixedImageType>

using	ImageRandomCoordinateSamplerPointer = typename ImageRandomCoordinateSamplerType::Pointer

using	ImageRandomCoordinateSamplerType = itk::ImageRandomCoordinateSampler<FixedImageType>

using	ImageRandomSamplerBasePointer = typename ImageRandomSamplerBaseType::Pointer

using	ImageRandomSamplerBaseType = itk::ImageRandomSamplerBase<FixedImageType>

using	ImageSampleContainerPointer = typename ImageSampleContainerType::Pointer

using	ImageSampleContainerType = typename ImageGridSamplerType::ImageSampleContainerType

using	ImageSamplerBasePointer = typename ImageSamplerBaseType::Pointer

using	ImageSamplerBaseType = itk::ImageSamplerBase<FixedImageType>

using	itkRegistrationType = typename RegistrationType::ITKBaseType

using	JacobianType = typename TransformType::JacobianType

using	JacobianValueType = typename JacobianType::ValueType

using	MovingImageType = typename RegistrationType::MovingImageType

using	NonZeroJacobianIndicesType = typename AdvancedTransformType::NonZeroJacobianIndicesType

using	RandomGeneratorPointer = typename RandomGeneratorType::Pointer

using	RandomGeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator

using	SettingsVectorType

using	TransformJacobianType = JacobianType

using	TransformType = typename itkRegistrationType::TransformType

Protected Types inherited from elastix::OptimizerBase< TElastix >
using	SettingsVectorType = typename std::vector<SettingsType>

Protected Member Functions
	AdaptiveStochasticGradientDescent ()

virtual void	AddRandomPerturbation (ParametersType &parameters, double sigma)

virtual void	AutomaticParameterEstimation ()

virtual void	AutomaticParameterEstimationOriginal ()

virtual void	AutomaticParameterEstimationUsingDisplacementDistribution ()

virtual void	GetScaledDerivativeWithExceptionHandling (const ParametersType &parameters, DerivativeType &derivative)

	itkStaticConstMacro (FixedImageDimension, unsigned int, FixedImageType::ImageDimension)

	itkStaticConstMacro (MovingImageDimension, unsigned int, MovingImageType::ImageDimension)

virtual void	SampleGradients (const ParametersType &mu0, double perturbationSigma, double &gg, double &ee)

	~AdaptiveStochasticGradientDescent () override=default

Protected Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
	AdaptiveStochasticGradientDescentOptimizer ()

void	UpdateCurrentTime () override

	~AdaptiveStochasticGradientDescentOptimizer () override=default

Protected Member Functions inherited from itk::StandardGradientDescentOptimizer
virtual double	Compute_a (double k) const

	StandardGradientDescentOptimizer ()

	~StandardGradientDescentOptimizer () override=default

Protected Member Functions inherited from itk::GradientDescentOptimizer2
	GradientDescentOptimizer2 ()

void	PrintSelf (std::ostream &os, Indent indent) const override

	~GradientDescentOptimizer2 () override=default

Protected Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
virtual void	GetScaledDerivative (const ParametersType &parameters, DerivativeType &derivative) const

virtual MeasureType	GetScaledValue (const ParametersType &parameters) const

virtual void	GetScaledValueAndDerivative (const ParametersType &parameters, MeasureType &value, DerivativeType &derivative) const

void	PrintSelf (std::ostream &os, Indent indent) const override

	ScaledSingleValuedNonLinearOptimizer ()

void	SetCurrentPosition (const ParametersType &param) override

virtual void	SetScaledCurrentPosition (const ParametersType &parameters)

	~ScaledSingleValuedNonLinearOptimizer () override=default

Protected Member Functions inherited from elastix::OptimizerBase< TElastix >
virtual bool	GetNewSamplesEveryIteration () const

	OptimizerBase ()=default

virtual void	SelectNewSamples ()

	~OptimizerBase () override=default

Protected Member Functions inherited from elastix::BaseComponentSE< TElastix >
	BaseComponentSE ()=default

	~BaseComponentSE () override=default

Protected Member Functions inherited from elastix::BaseComponent
	BaseComponent ()=default

virtual	~BaseComponent ()=default

Protected Attributes
AdvancedTransformPointer	m_AdvancedTransform

SizeValueType	m_NumberOfGradientMeasurements

SizeValueType	m_NumberOfJacobianMeasurements

SizeValueType	m_NumberOfSamplesForExactGradient

RandomGeneratorPointer	m_RandomGenerator

SettingsVectorType	m_SettingsVector

double	m_SigmoidScaleFactor

Protected Attributes inherited from itk::AdaptiveStochasticGradientDescentOptimizer
DerivativeType	m_PreviousGradient {}

Protected Attributes inherited from itk::StandardGradientDescentOptimizer
double	m_CurrentTime { 0.0 }

bool	m_UseConstantStep { false }

Protected Attributes inherited from itk::GradientDescentOptimizer2
DerivativeType	m_Gradient {}

DerivativeType	m_SearchDirection {}

StopConditionType	m_StopCondition { MaximumNumberOfIterations }

Protected Attributes inherited from itk::ScaledSingleValuedNonLinearOptimizer
ScaledCostFunctionPointer	m_ScaledCostFunction {}

ParametersType	m_ScaledCurrentPosition {}

Protected Attributes inherited from elastix::BaseComponentSE< TElastix >
Configuration::ConstPointer	m_Configuration {}

itk::WeakPointer< TElastix >	m_Elastix {}

RegistrationType *	m_Registration {}

Additional Inherited Members
Static Protected Member Functions inherited from elastix::OptimizerBase< TElastix >
static void	PrintSettingsVector (const SettingsVectorType &settings)

Member Typedef Documentation

◆ AdvancedTransformPointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformPointer = typename AdvancedTransformType::Pointer

protected

Definition at line 330 of file elxAdaptiveStochasticGradientDescent.h.

◆ AdvancedTransformType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformType

protected

Initial value:

itk::AdvancedTransform<CoordinateRepresentationType, Self::FixedImageDimension, Self::MovingImageDimension>

itk::AdvancedTransform

Transform maps points, vectors and covariant vectors from an input space to an output space.

Definition itkAdvancedTransform.h:84

Definition at line 328 of file elxAdaptiveStochasticGradientDescent.h.

◆ ComputeDisplacementDistributionType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ComputeDisplacementDistributionType = itk::ComputeDisplacementDistribution<FixedImageType, TransformType>

protected

Definition at line 305 of file elxAdaptiveStochasticGradientDescent.h.

◆ ComputeJacobianTermsType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ComputeJacobianTermsType = itk::ComputeJacobianTerms<FixedImageType, TransformType>

protected

Definition at line 301 of file elxAdaptiveStochasticGradientDescent.h.

◆ ConstPointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ConstPointer = itk::SmartPointer<const Self>

Definition at line 205 of file elxAdaptiveStochasticGradientDescent.h.

◆ CoordinateRepresentationType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::CoordinateRepresentationType = typename TransformType::ScalarType

protected

Definition at line 327 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageIndexType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageIndexType = typename FixedImageType::IndexType

protected

Definition at line 296 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImagePointType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImagePointType = typename FixedImageType::PointType

protected

Definition at line 297 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageRegionType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageRegionType = typename FixedImageType::RegionType

protected

Definition at line 295 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageType = typename RegistrationType::FixedImageType

protected

Protected typedefs

Definition at line 292 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageGridSamplerPointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerPointer = typename ImageGridSamplerType::Pointer

protected

Definition at line 315 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageGridSamplerType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerType = itk::ImageGridSampler<FixedImageType>

protected

Definition at line 314 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomCoordinateSamplerPointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomCoordinateSamplerPointer = typename ImageRandomCoordinateSamplerType::Pointer

protected

Definition at line 313 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomCoordinateSamplerType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomCoordinateSamplerType = itk::ImageRandomCoordinateSampler<FixedImageType>

protected

Definition at line 312 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomSamplerBasePointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomSamplerBasePointer = typename ImageRandomSamplerBaseType::Pointer

protected

Definition at line 311 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomSamplerBaseType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomSamplerBaseType = itk::ImageRandomSamplerBase<FixedImageType>

protected

Definition at line 310 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSampleContainerPointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSampleContainerPointer = typename ImageSampleContainerType::Pointer

protected

Definition at line 317 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSampleContainerType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSampleContainerType = typename ImageGridSamplerType::ImageSampleContainerType

protected

Definition at line 316 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSamplerBasePointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBasePointer = typename ImageSamplerBaseType::Pointer

protected

Definition at line 309 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSamplerBaseType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBaseType = itk::ImageSamplerBase<FixedImageType>

protected

Samplers:

Definition at line 308 of file elxAdaptiveStochasticGradientDescent.h.

◆ ITKBaseType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::ITKBaseType = typename Superclass2::ITKBaseType

Definition at line 227 of file elxAdaptiveStochasticGradientDescent.h.

◆ itkRegistrationType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::itkRegistrationType = typename RegistrationType::ITKBaseType

protected

Definition at line 298 of file elxAdaptiveStochasticGradientDescent.h.

◆ JacobianType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianType = typename TransformType::JacobianType

protected

Definition at line 300 of file elxAdaptiveStochasticGradientDescent.h.

◆ JacobianValueType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianValueType = typename JacobianType::ValueType

protected

Definition at line 302 of file elxAdaptiveStochasticGradientDescent.h.

◆ MovingImageType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::MovingImageType = typename RegistrationType::MovingImageType

protected

Definition at line 293 of file elxAdaptiveStochasticGradientDescent.h.

◆ NonZeroJacobianIndicesType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::NonZeroJacobianIndicesType = typename AdvancedTransformType::NonZeroJacobianIndicesType

protected

Definition at line 331 of file elxAdaptiveStochasticGradientDescent.h.

◆ Pointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::Pointer = itk::SmartPointer<Self>

Definition at line 204 of file elxAdaptiveStochasticGradientDescent.h.

◆ RandomGeneratorPointer

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorPointer = typename RandomGeneratorType::Pointer

protected

Definition at line 321 of file elxAdaptiveStochasticGradientDescent.h.

◆ RandomGeneratorType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator

protected

Other protected typedefs

Definition at line 320 of file elxAdaptiveStochasticGradientDescent.h.

◆ Self

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::Self = AdaptiveStochasticGradientDescent

Standard ITK.

Definition at line 201 of file elxAdaptiveStochasticGradientDescent.h.

◆ SettingsVectorType

template<class TElastix >

using elastix::OptimizerBase< TElastix >::SettingsVectorType

protected

Definition at line 133 of file elxOptimizerBase.h.

◆ SizeValueType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::SizeValueType = itk::SizeValueType

Definition at line 228 of file elxAdaptiveStochasticGradientDescent.h.

◆ Superclass1

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass1 = AdaptiveStochasticGradientDescentOptimizer

Definition at line 202 of file elxAdaptiveStochasticGradientDescent.h.

◆ Superclass2

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass2 = OptimizerBase<TElastix>

Definition at line 203 of file elxAdaptiveStochasticGradientDescent.h.

◆ TransformJacobianType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformJacobianType = JacobianType

protected

Typedefs for support of sparse Jacobians and AdvancedTransforms.

Definition at line 324 of file elxAdaptiveStochasticGradientDescent.h.

◆ TransformType

template<class TElastix >

using elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformType = typename itkRegistrationType::TransformType

protected

Definition at line 299 of file elxAdaptiveStochasticGradientDescent.h.

Constructor & Destructor Documentation

◆ AdaptiveStochasticGradientDescent()

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::AdaptiveStochasticGradientDescent ( )

protected

◆ ~AdaptiveStochasticGradientDescent()

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::~AdaptiveStochasticGradientDescent ( )

overrideprotecteddefault

Member Function Documentation

◆ AddRandomPerturbation()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AddRandomPerturbation	(	ParametersType &	parameters,
		double	sigma )

protectedvirtual

Helper function that adds a random perturbation delta to the input parameters, with delta ~ sigma * N(0,I). Used by SampleGradients.

◆ AfterEachIteration()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachIteration ( )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AfterEachResolution()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachResolution ( )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AfterRegistration()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterRegistration ( )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AutomaticParameterEstimation()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimation ( )

protectedvirtual

Select different method to estimate some reasonable values for the parameters SP_a, SP_alpha (=1), SigmoidMin, SigmoidMax (=1), and SigmoidScale.

◆ AutomaticParameterEstimationOriginal()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimationOriginal ( )

protectedvirtual

Original estimation method to get the reasonable values for the parameters SP_a, SP_alpha (=1), SigmoidMin, SigmoidMax (=1), and SigmoidScale.

◆ AutomaticParameterEstimationUsingDisplacementDistribution()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimationUsingDisplacementDistribution ( )

protectedvirtual

Estimates some reasonable values for the parameters using displacement distribution SP_a, SP_alpha (=1)

◆ BeforeEachResolution()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeEachResolution ( )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ BeforeRegistration()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeRegistration ( )

overridevirtual

Methods invoked by elastix, in which parameters can be set and progress information can be printed.

Reimplemented from elastix::BaseComponent.

◆ elxClassNameMacro()

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::elxClassNameMacro ( "AdaptiveStochasticGradientDescent< TElastix >" )

Name of this class. Use this name in the parameter file to select this specific optimizer. example: (Optimizer "AdaptiveStochasticGradientDescent")

◆ GetAutomaticParameterEstimation()

template<class TElastix >

virtual bool elastix::AdaptiveStochasticGradientDescent< TElastix >::GetAutomaticParameterEstimation ( ) const

virtual

◆ GetClassName()

template<class TElastix >

virtual const char * elastix::AdaptiveStochasticGradientDescent< TElastix >::GetClassName ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::AdaptiveStochasticGradientDescentOptimizer.

◆ GetMaximumNumberOfSamplingAttempts()

template<class TElastix >

virtual const SizeValueType & elastix::AdaptiveStochasticGradientDescent< TElastix >::GetMaximumNumberOfSamplingAttempts ( )

virtual

Get the MaximumNumberOfSamplingAttempts.

◆ GetMaximumStepLength()

template<class TElastix >

virtual double elastix::AdaptiveStochasticGradientDescent< TElastix >::GetMaximumStepLength ( ) const

virtual

◆ GetScaledDerivativeWithExceptionHandling()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::GetScaledDerivativeWithExceptionHandling	(	const ParametersType &	parameters,
		DerivativeType &	derivative )

protectedvirtual

Helper function, which calls GetScaledValueAndDerivative and does some exception handling. Used by SampleGradients.

◆ ITK_DISALLOW_COPY_AND_MOVE()

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::ITK_DISALLOW_COPY_AND_MOVE ( AdaptiveStochasticGradientDescent< TElastix > )

◆ itkStaticConstMacro() [1/2]

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::itkStaticConstMacro	(	FixedImageDimension	,
		unsigned int	,
		FixedImageType::ImageDimension	)

protected

◆ itkStaticConstMacro() [2/2]

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::itkStaticConstMacro	(	MovingImageDimension	,
		unsigned int	,
		MovingImageType::ImageDimension	)

protected

◆ MetricErrorResponse()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::MetricErrorResponse ( itk::ExceptionObject & err )

override

Stop optimization and pass on exception.

◆ New()

template<class TElastix >

static Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::New ( )

static

Method for creation through the object factory.

◆ ResumeOptimization()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::ResumeOptimization ( )

overridevirtual

If automatic gain estimation is desired, then estimate SP_a, SP_alpha SigmoidScale, SigmoidMax, SigmoidMin. After that call Superclass' implementation.

Reimplemented from itk::GradientDescentOptimizer2.

◆ SampleGradients()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SampleGradients	(	const ParametersType &	mu0,
		double	perturbationSigma,
		double &	gg,
		double &	ee )

protectedvirtual

Measure some derivatives, exact and approximated. Returns the squared magnitude of the gradient and approximation error. Needed for the automatic parameter estimation. Gradients are measured at position mu_n, which are generated according to: mu_n - mu_0 ~ N(0, perturbationSigma^2 I ); gg = g^T g, etc.

◆ SetAutomaticParameterEstimation()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SetAutomaticParameterEstimation ( bool _arg )

virtual

Set/Get whether automatic parameter estimation is desired. If true, make sure to set the maximum step length.

The following parameters are automatically determined: SP_a, SP_alpha (=1), SigmoidMin, SigmoidMax (=1), SigmoidScale. A usually suitable value for SP_A is 20, which is the default setting, if not specified by the user.

◆ SetMaximumNumberOfSamplingAttempts()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SetMaximumNumberOfSamplingAttempts ( SizeValueType _arg )

virtual

Set the MaximumNumberOfSamplingAttempts.

◆ SetMaximumStepLength()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SetMaximumStepLength ( double _arg )

virtual

Set/Get maximum step length.

◆ StartOptimization()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::StartOptimization ( )

override

Check if any scales are set, and set the UseScales flag on or off; after that call the superclass' implementation.

Field Documentation

◆ elxOverrideGetSelfMacro

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::elxOverrideGetSelfMacro

private

Definition at line 395 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AdvancedTransform

template<class TElastix >

AdvancedTransformPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AdvancedTransform

protected

The transform stored as AdvancedTransform

Definition at line 345 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AutomaticParameterEstimation

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimation

private

Definition at line 397 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AutomaticParameterEstimationDone

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimationDone

private

Definition at line 405 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_CurrentNumberOfSamplingAttempts

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_CurrentNumberOfSamplingAttempts

private

Definition at line 403 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaxBandCovSize

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaxBandCovSize

private

Private variables for band size estimation of covariance matrix.

Definition at line 408 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumNumberOfSamplingAttempts

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumNumberOfSamplingAttempts

private

Private variables for the sampling attempts.

Definition at line 402 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumStepLength

template<class TElastix >

double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLength

private

Definition at line 398 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumStepLengthRatio

template<class TElastix >

double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLengthRatio

private

Definition at line 399 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfBandStructureSamples

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfBandStructureSamples

private

Definition at line 409 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfGradientMeasurements

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfGradientMeasurements

protected

Some options for automatic parameter estimation.

Definition at line 340 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfJacobianMeasurements

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfJacobianMeasurements

protected

Definition at line 341 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfSamplesForExactGradient

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfSamplesForExactGradient

protected

Definition at line 342 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_OriginalButSigmoidToDefault

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_OriginalButSigmoidToDefault

private

Definition at line 413 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_PreviousErrorAtIteration

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_PreviousErrorAtIteration

private

Definition at line 404 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_RandomGenerator

template<class TElastix >

RandomGeneratorPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_RandomGenerator

protected

RandomGenerator for AddRandomPerturbation.

Definition at line 348 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_SettingsVector

template<class TElastix >

SettingsVectorType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SettingsVector

protected

Variable to store the automatically determined settings for each resolution.

Definition at line 337 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_SigmoidScaleFactor

template<class TElastix >

double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SigmoidScaleFactor

protected

Definition at line 350 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_UseNoiseCompensation

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_UseNoiseCompensation

private

The flag of using noise compensation.

Definition at line 412 of file elxAdaptiveStochasticGradientDescent.h.

Private Attributes
	elxOverrideGetSelfMacro

bool	m_AutomaticParameterEstimation

bool	m_AutomaticParameterEstimationDone

SizeValueType	m_CurrentNumberOfSamplingAttempts

SizeValueType	m_MaxBandCovSize

SizeValueType	m_MaximumNumberOfSamplingAttempts

double	m_MaximumStepLength

double	m_MaximumStepLengthRatio

SizeValueType	m_NumberOfBandStructureSamples

bool	m_OriginalButSigmoidToDefault

SizeValueType	m_PreviousErrorAtIteration

bool	m_UseNoiseCompensation

Detailed Description

Public Types

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ AdvancedTransformPointer

◆ AdvancedTransformType

◆ ComputeDisplacementDistributionType

◆ ComputeJacobianTermsType

◆ ConstPointer

◆ CoordinateRepresentationType

◆ FixedImageIndexType

◆ FixedImagePointType

◆ FixedImageRegionType

◆ FixedImageType

◆ ImageGridSamplerPointer

◆ ImageGridSamplerType

◆ ImageRandomCoordinateSamplerPointer

◆ ImageRandomCoordinateSamplerType

◆ ImageRandomSamplerBasePointer

◆ ImageRandomSamplerBaseType

◆ ImageSampleContainerPointer

◆ ImageSampleContainerType

◆ ImageSamplerBasePointer

◆ ImageSamplerBaseType

◆ ITKBaseType

◆ itkRegistrationType

◆ JacobianType

◆ JacobianValueType

◆ MovingImageType

◆ NonZeroJacobianIndicesType

◆ Pointer

◆ RandomGeneratorPointer

◆ RandomGeneratorType

◆ Self

◆ SettingsVectorType

◆ SizeValueType

◆ Superclass1

◆ Superclass2

◆ TransformJacobianType

◆ TransformType

Constructor & Destructor Documentation

◆ AdaptiveStochasticGradientDescent()

◆ ~AdaptiveStochasticGradientDescent()

Member Function Documentation

◆ AddRandomPerturbation()

◆ AfterEachIteration()

◆ AfterEachResolution()

◆ AfterRegistration()

◆ AutomaticParameterEstimation()

◆ AutomaticParameterEstimationOriginal()

◆ AutomaticParameterEstimationUsingDisplacementDistribution()

◆ BeforeEachResolution()

◆ BeforeRegistration()

◆ elxClassNameMacro()

◆ GetAutomaticParameterEstimation()

◆ GetClassName()

◆ GetMaximumNumberOfSamplingAttempts()

◆ GetMaximumStepLength()

◆ GetScaledDerivativeWithExceptionHandling()

◆ ITK_DISALLOW_COPY_AND_MOVE()

◆ itkStaticConstMacro() [1/2]

◆ itkStaticConstMacro() [2/2]

◆ MetricErrorResponse()

◆ New()

◆ ResumeOptimization()

◆ SampleGradients()

◆ SetAutomaticParameterEstimation()

◆ SetMaximumNumberOfSamplingAttempts()

◆ SetMaximumStepLength()

◆ StartOptimization()

Field Documentation

◆ elxOverrideGetSelfMacro

◆ m_AdvancedTransform

◆ m_AutomaticParameterEstimation