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elastix::AdaptiveStochasticGradientDescent< TElastix > Class Template Reference

#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.P.F. Lelieveldt, M.Staring "Fast automatic estimation of the optimization step size for nonrigid image registration," SPIE Medical Imaging: Image Processing,February, 2014. http://elastix.isi.uu.nl/marius/publications/2014_c_SPIEMI.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 191 of file elxAdaptiveStochasticGradientDescent.h.

Inheritance diagram for elastix::AdaptiveStochasticGradientDescent< TElastix >:
Inheritance graph
[legend]

Data Structures

struct  SettingsType
 

Public Types

typedef Superclass2::ConfigurationPointer ConfigurationPointer
 
typedef Superclass2::ConfigurationType ConfigurationType
 
typedef itk::SmartPointer< const SelfConstPointer
 
typedef Superclass1::CostFunctionPointer CostFunctionPointer
 
typedef Superclass1::CostFunctionType CostFunctionType
 
typedef Superclass2::ElastixPointer ElastixPointer
 
typedef Superclass2::ElastixType ElastixType
 
typedef Superclass2::ITKBaseType ITKBaseType
 
typedef Superclass1::ParametersType ParametersType
 
typedef itk::SmartPointer< SelfPointer
 
typedef Superclass2::RegistrationPointer RegistrationPointer
 
typedef Superclass2::RegistrationType RegistrationType
 
typedef AdaptiveStochasticGradientDescent Self
 
typedef itk::SizeValueType SizeValueType
 
typedef Superclass1::StopConditionType StopConditionType
 
typedef AdaptiveStochasticGradientDescentOptimizer Superclass1
 
typedef OptimizerBase< TElastix > Superclass2
 
- Public Types inherited from itk::AdaptiveStochasticGradientDescentOptimizer
typedef SmartPointer< const SelfConstPointer
 
typedef Superclass::CostFunctionType CostFunctionType
 
typedef Superclass::DerivativeType DerivativeType
 
typedef Superclass::MeasureType MeasureType
 
typedef Superclass::ParametersType ParametersType
 
typedef SmartPointer< SelfPointer
 
typedef Superclass::ScaledCostFunctionPointer ScaledCostFunctionPointer
 
typedef Superclass::ScaledCostFunctionType ScaledCostFunctionType
 
typedef Superclass::ScalesType ScalesType
 
typedef AdaptiveStochasticGradientDescentOptimizer Self
 
typedef Superclass::StopConditionType StopConditionType
 
typedef StandardGradientDescentOptimizer Superclass
 
- Public Types inherited from itk::StandardGradientDescentOptimizer
typedef SmartPointer< const SelfConstPointer
 
typedef Superclass::CostFunctionType CostFunctionType
 
typedef Superclass::DerivativeType DerivativeType
 
typedef Superclass::MeasureType MeasureType
 
typedef Superclass::ParametersType ParametersType
 
typedef SmartPointer< SelfPointer
 
typedef Superclass::ScaledCostFunctionPointer ScaledCostFunctionPointer
 
typedef Superclass::ScaledCostFunctionType ScaledCostFunctionType
 
typedef Superclass::ScalesType ScalesType
 
typedef StandardGradientDescentOptimizer Self
 
typedef Superclass::StopConditionType StopConditionType
 
typedef GradientDescentOptimizer2 Superclass
 
- Public Types inherited from itk::GradientDescentOptimizer2
typedef SmartPointer< const SelfConstPointer
 
typedef Superclass::CostFunctionType CostFunctionType
 
typedef Superclass::DerivativeType DerivativeType
 
typedef Superclass::MeasureType MeasureType
 
typedef Superclass::ParametersType ParametersType
 
typedef SmartPointer< SelfPointer
 
typedef Superclass::ScaledCostFunctionPointer ScaledCostFunctionPointer
 
typedef Superclass::ScaledCostFunctionType ScaledCostFunctionType
 
typedef Superclass::ScalesType ScalesType
 
typedef GradientDescentOptimizer2 Self
 
enum  StopConditionType { MaximumNumberOfIterations, MetricError, MinimumStepSize }
 
typedef ScaledSingleValuedNonLinearOptimizer Superclass
 
- Public Types inherited from itk::ScaledSingleValuedNonLinearOptimizer
typedef SmartPointer< const SelfConstPointer
 
typedef Superclass::CostFunctionType CostFunctionType
 
typedef Superclass::DerivativeType DerivativeType
 
typedef Superclass::MeasureType MeasureType
 
typedef Superclass::ParametersType ParametersType
 
typedef SmartPointer< SelfPointer
 
typedef ScaledCostFunctionType::Pointer ScaledCostFunctionPointer
 
typedef ScaledSingleValuedCostFunction ScaledCostFunctionType
 
typedef NonLinearOptimizer::ScalesType ScalesType
 
typedef ScaledSingleValuedNonLinearOptimizer Self
 
typedef SingleValuedNonLinearOptimizer Superclass
 
- Public Types inherited from elastix::OptimizerBase< TElastix >
typedef Superclass::ConfigurationPointer ConfigurationPointer
 
typedef Superclass::ConfigurationType ConfigurationType
 
typedef Superclass::ElastixPointer ElastixPointer
 
typedef Superclass::ElastixType ElastixType
 
typedef itk::Optimizer ITKBaseType
 
typedef ITKBaseType::ParametersType ParametersType
 
typedef Superclass::RegistrationPointer RegistrationPointer
 
typedef Superclass::RegistrationType RegistrationType
 
typedef OptimizerBase Self
 
typedef BaseComponentSE< TElastix > Superclass
 
- Public Types inherited from elastix::BaseComponentSE< TElastix >
typedef ElastixType::ConfigurationPointer ConfigurationPointer
 
typedef ElastixType::ConfigurationType ConfigurationType
 
typedef ElastixType::Pointer ElastixPointer
 
typedef TElastix ElastixType
 
typedef RegistrationTypeRegistrationPointer
 
typedef ElastixType::RegistrationBaseType RegistrationType
 
typedef BaseComponentSE Self
 
typedef BaseComponent Superclass
 

Public Member Functions

virtual void AfterEachIteration (void)
 
virtual void AfterEachResolution (void)
 
virtual void AfterRegistration (void)
 
virtual void BeforeEachResolution (void)
 
virtual void BeforeRegistration (void)
 
 elxClassNameMacro ("AdaptiveStochasticGradientDescent")
 
virtual bool GetAutomaticParameterEstimation () const
 
virtual const char * GetClassName () const
 
virtual const SizeValueTypeGetMaximumNumberOfSamplingAttempts ()
 
virtual double GetMaximumStepLength () const
 
virtual void MetricErrorResponse (itk::ExceptionObject &err)
 
virtual void ResumeOptimization (void)
 
virtual void SetAutomaticParameterEstimation (bool _arg)
 
virtual void SetMaximumNumberOfSamplingAttempts (SizeValueType _arg)
 
virtual void SetMaximumStepLength (double _arg)
 
virtual void StartOptimization (void)
 
- Public Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
virtual double GetSigmoidMax () const
 
virtual double GetSigmoidMin () const
 
virtual double GetSigmoidScale () const
 
virtual bool GetUseAdaptiveStepSizes () const
 
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
virtual void AdvanceOneStep (void)
 
virtual double GetCurrentTime () const
 
virtual double GetInitialTime () const
 
virtual double GetParam_a () const
 
virtual double GetParam_A () const
 
virtual double GetParam_alpha () const
 
virtual void ResetCurrentTimeToInitialTime (void)
 
virtual void SetInitialTime (double _arg)
 
virtual void SetParam_a (double _arg)
 
virtual void SetParam_A (double _arg)
 
virtual void SetParam_alpha (double _arg)
 
- Public Member Functions inherited from itk::GradientDescentOptimizer2
virtual unsigned int GetCurrentIteration () const
 
virtual const DerivativeTypeGetGradient ()
 
virtual const doubleGetLearningRate ()
 
virtual const unsigned long & GetNumberOfIterations ()
 
virtual const StopConditionTypeGetStopCondition ()
 
virtual const doubleGetValue ()
 
virtual void MetricErrorResponse (ExceptionObject &err)
 
virtual void SetLearningRate (double _arg)
 
virtual void SetNumberOfIterations (unsigned long _arg)
 
void SetNumberOfThreads (ThreadIdType numberOfThreads)
 
virtual void SetUseEigen (bool _arg)
 
virtual void SetUseMultiThread (bool _arg)
 
virtual void SetUseOpenMP (bool _arg)
 
virtual void StopOptimization (void)
 
- Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
virtual const ParametersTypeGetCurrentPosition (void) const
 
virtual bool GetMaximize () const
 
virtual const ScaledCostFunctionTypeGetScaledCostFunction ()
 
virtual const ParametersTypeGetScaledCurrentPosition ()
 
bool GetUseScales (void) const
 
virtual void InitializeScales (void)
 
virtual void MaximizeOff ()
 
virtual void MaximizeOn ()
 
virtual void SetCostFunction (CostFunctionType *costFunction)
 
virtual void SetMaximize (bool _arg)
 
virtual void SetUseScales (bool arg)
 
- Public Member Functions inherited from elastix::OptimizerBase< TElastix >
virtual void AfterRegistrationBase (void)
 
virtual void BeforeEachResolutionBase ()
 
virtual ITKBaseTypeGetAsITKBaseType (void)
 
virtual const ITKBaseTypeGetAsITKBaseType (void) const
 
virtual void SetCurrentPositionPublic (const ParametersType &param)
 
virtual void SetSinusScales (double amplitude, double frequency, unsigned long numberOfParameters)
 
- Public Member Functions inherited from elastix::BaseComponentSE< TElastix >
virtual ConfigurationTypeGetConfiguration (void) const
 
virtual ElastixTypeGetElastix (void) const
 
virtual RegistrationPointer GetRegistration (void) const
 
virtual void SetConfiguration (ConfigurationType *_arg)
 
virtual void SetElastix (ElastixType *_arg)
 
- Public Member Functions inherited from elastix::BaseComponent
virtual void AfterEachIterationBase (void)
 
virtual void AfterEachResolutionBase (void)
 
virtual int BeforeAll (void)
 
virtual int BeforeAllBase (void)
 
virtual void BeforeRegistrationBase (void)
 
std::string ConvertSecondsToDHMS (const double totalSeconds, const unsigned int precision) const
 
virtual const char * elxGetClassName (void) const
 
virtual const char * GetComponentLabel (void) const
 
virtual 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 ()
 

Protected Types

typedef AdvancedTransformType::Pointer AdvancedTransformPointer
 
typedef itk::AdvancedTransform< CoordinateRepresentationType, itkGetStaticConstMacro(FixedImageDimension), itkGetStaticConstMacro(MovingImageDimension) > AdvancedTransformType
 
typedef itk::ComputeDisplacementDistribution< FixedImageType, TransformTypeComputeDisplacementDistributionType
 
typedef itk::ComputeJacobianTerms< FixedImageType, TransformTypeComputeJacobianTermsType
 
typedef TransformType::ScalarType CoordinateRepresentationType
 
typedef FixedImageType::IndexType FixedImageIndexType
 
typedef FixedImageType::PointType FixedImagePointType
 
typedef FixedImageType::RegionType FixedImageRegionType
 
typedef RegistrationType::FixedImageType FixedImageType
 
typedef ImageGridSamplerType::Pointer ImageGridSamplerPointer
 
typedef itk::ImageGridSampler< FixedImageTypeImageGridSamplerType
 
typedef ImageRandomCoordinateSamplerType::Pointer ImageRandomCoordinateSamplerPointer
 
typedef itk::ImageRandomCoordinateSampler< FixedImageTypeImageRandomCoordinateSamplerType
 
typedef ImageRandomSamplerBaseType::Pointer ImageRandomSamplerBasePointer
 
typedef itk::ImageRandomSamplerBase< FixedImageTypeImageRandomSamplerBaseType
 
typedef ImageSampleContainerType::Pointer ImageSampleContainerPointer
 
typedef ImageGridSamplerType::ImageSampleContainerType ImageSampleContainerType
 
typedef ImageSamplerBaseType::Pointer ImageSamplerBasePointer
 
typedef itk::ImageSamplerBase< FixedImageTypeImageSamplerBaseType
 
typedef RegistrationType::ITKBaseType itkRegistrationType
 
typedef TransformType::JacobianType JacobianType
 
typedef JacobianType::ValueType JacobianValueType
 
typedef RegistrationType::MovingImageType MovingImageType
 
typedef AdvancedTransformType::NonZeroJacobianIndicesType NonZeroJacobianIndicesType
 
typedef ProgressCommand::Pointer ProgressCommandPointer
 
typedef ProgressCommand ProgressCommandType
 
typedef RandomGeneratorType::Pointer RandomGeneratorPointer
 
typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType
 
typedef std::vector< SettingsType > SettingsVectorType
 
typedef JacobianType TransformJacobianType
 
typedef itkRegistrationType::TransformType TransformType
 
- Protected Types inherited from itk::GradientDescentOptimizer2
typedef itk::MultiThreader ThreaderType
 
typedef ThreaderType::ThreadInfoStruct ThreadInfoType
 

Protected Member Functions

 AdaptiveStochasticGradientDescent ()
 
virtual void AddRandomPerturbation (ParametersType &parameters, double sigma)
 
virtual void AutomaticParameterEstimation (void)
 
virtual void AutomaticParameterEstimationOriginal (void)
 
virtual void AutomaticParameterEstimationUsingDisplacementDistribution (void)
 
virtual void CheckForAdvancedTransform (void)
 
virtual void GetScaledDerivativeWithExceptionHandling (const ParametersType &parameters, DerivativeType &derivative)
 
 itkStaticConstMacro (FixedImageDimension, unsigned int, FixedImageType::ImageDimension)
 
 itkStaticConstMacro (MovingImageDimension, unsigned int, MovingImageType::ImageDimension)
 
virtual void PrintSettingsVector (const SettingsVectorType &settings) const
 
virtual void SampleGradients (const ParametersType &mu0, double perturbationSigma, double &gg, double &ee)
 
virtual ~AdaptiveStochasticGradientDescent ()
 
- Protected Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
 AdaptiveStochasticGradientDescentOptimizer ()
 
virtual void UpdateCurrentTime (void)
 
virtual ~AdaptiveStochasticGradientDescentOptimizer ()
 
- Protected Member Functions inherited from itk::StandardGradientDescentOptimizer
virtual double Compute_a (double k) const
 
 StandardGradientDescentOptimizer ()
 
virtual ~StandardGradientDescentOptimizer ()
 
- Protected Member Functions inherited from itk::GradientDescentOptimizer2
 GradientDescentOptimizer2 ()
 
void PrintSelf (std::ostream &os, Indent indent) const
 
virtual ~GradientDescentOptimizer2 ()
 
- 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
 
 ScaledSingleValuedNonLinearOptimizer ()
 
virtual void SetCurrentPosition (const ParametersType &param)
 
virtual void SetScaledCurrentPosition (const ParametersType &parameters)
 
virtual ~ScaledSingleValuedNonLinearOptimizer ()
 
- Protected Member Functions inherited from elastix::OptimizerBase< TElastix >
virtual bool GetNewSamplesEveryIteration (void) const
 
 OptimizerBase ()
 
virtual void SelectNewSamples (void)
 
virtual ~OptimizerBase ()
 
- Protected Member Functions inherited from elastix::BaseComponentSE< TElastix >
 BaseComponentSE ()
 
virtual ~BaseComponentSE ()
 
- Protected Member Functions inherited from elastix::BaseComponent
 BaseComponent ()
 
virtual ~BaseComponent ()
 

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
 
- Protected Attributes inherited from itk::GradientDescentOptimizer2
unsigned long m_CurrentIteration
 
DerivativeType m_Gradient
 
double m_LearningRate
 
unsigned long m_NumberOfIterations
 
bool m_Stop
 
StopConditionType m_StopCondition
 
ThreaderType::Pointer m_Threader
 
double m_Value
 
- Protected Attributes inherited from itk::ScaledSingleValuedNonLinearOptimizer
ScaledCostFunctionPointer m_ScaledCostFunction
 
ParametersType m_ScaledCurrentPosition
 
- Protected Attributes inherited from elastix::BaseComponentSE< TElastix >
ConfigurationPointer m_Configuration
 
ElastixPointer m_Elastix
 
RegistrationPointer m_Registration
 

Private Member Functions

 AdaptiveStochasticGradientDescent (const Self &)
 
void operator= (const Self &)
 

Private Attributes

bool m_AutomaticParameterEstimation
 
bool m_AutomaticParameterEstimationDone
 
SizeValueType m_CurrentNumberOfSamplingAttempts
 
SizeValueType m_MaxBandCovSize
 
SizeValueType m_MaximumNumberOfSamplingAttempts
 
double m_MaximumStepLength
 
SizeValueType m_NumberOfBandStructureSamples
 
bool m_OriginalButSigmoidToDefault
 
SizeValueType m_PreviousErrorAtIteration
 
bool m_UseNoiseCompensation
 

Member Typedef Documentation

Definition at line 336 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::AdvancedTransform< CoordinateRepresentationType, itkGetStaticConstMacro( FixedImageDimension ), itkGetStaticConstMacro( MovingImageDimension ) > elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformType
protected

Definition at line 335 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 303 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 297 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 226 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 225 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::SmartPointer< const Self > elastix::AdaptiveStochasticGradientDescent< TElastix >::ConstPointer

Definition at line 202 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef TransformType::ScalarType elastix::AdaptiveStochasticGradientDescent< TElastix >::CoordinateRepresentationType
protected

Definition at line 331 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef Superclass1::CostFunctionPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::CostFunctionPointer

Definition at line 219 of file elxAdaptiveStochasticGradientDescent.h.

Typedef's inherited from Superclass1.

Definition at line 218 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 224 of file elxAdaptiveStochasticGradientDescent.h.

Typedef's inherited from Superclass2.

Definition at line 223 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef FixedImageType::IndexType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageIndexType
protected

Definition at line 291 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef FixedImageType::PointType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImagePointType
protected

Definition at line 292 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef FixedImageType::RegionType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageRegionType
protected

Definition at line 290 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef RegistrationType::FixedImageType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageType
protected

Protected typedefs

Definition at line 282 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 316 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::ImageGridSampler< FixedImageType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerType
protected

Definition at line 315 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 314 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 312 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 310 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 308 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 319 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 318 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 307 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::ImageSamplerBase< FixedImageType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBaseType
protected

Samplers:

Definition at line 306 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 229 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef RegistrationType::ITKBaseType elastix::AdaptiveStochasticGradientDescent< TElastix >::itkRegistrationType
protected

Definition at line 293 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef TransformType::JacobianType elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianType
protected

Definition at line 295 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef JacobianType::ValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianValueType
protected

Definition at line 298 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef RegistrationType::MovingImageType elastix::AdaptiveStochasticGradientDescent< TElastix >::MovingImageType
protected

Definition at line 288 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 338 of file elxAdaptiveStochasticGradientDescent.h.

Typedef for the ParametersType.

Definition at line 233 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::SmartPointer< Self > elastix::AdaptiveStochasticGradientDescent< TElastix >::Pointer

Definition at line 201 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef ProgressCommand::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ProgressCommandPointer
protected

Definition at line 325 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef ProgressCommand elastix::AdaptiveStochasticGradientDescent< TElastix >::ProgressCommandType
protected

Definition at line 324 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef RandomGeneratorType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorPointer
protected

Definition at line 323 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::Statistics::MersenneTwisterRandomVariateGenerator elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorType
protected

Other protected typedefs

Definition at line 322 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 228 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 227 of file elxAdaptiveStochasticGradientDescent.h.

Standard ITK.

Definition at line 198 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef std::vector< SettingsType > elastix::AdaptiveStochasticGradientDescent< TElastix >::SettingsVectorType
protected

Definition at line 300 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itk::SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::SizeValueType

Definition at line 230 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 220 of file elxAdaptiveStochasticGradientDescent.h.

Definition at line 199 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef OptimizerBase< TElastix > elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass2

Definition at line 200 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef JacobianType elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformJacobianType
protected

Typedefs for support of sparse Jacobians and AdvancedTransforms.

Definition at line 328 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
typedef itkRegistrationType::TransformType elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformType
protected

Definition at line 294 of file elxAdaptiveStochasticGradientDescent.h.

Constructor & Destructor Documentation

template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::AdaptiveStochasticGradientDescent ( )
protected
template<class TElastix >
virtual elastix::AdaptiveStochasticGradientDescent< TElastix >::~AdaptiveStochasticGradientDescent ( )
inlineprotectedvirtual

Definition at line 341 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::AdaptiveStochasticGradientDescent ( const Self )
private

Member Function Documentation

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.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachIteration ( void  )
virtual

Reimplemented from elastix::BaseComponent.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachResolution ( void  )
virtual

Reimplemented from elastix::BaseComponent.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterRegistration ( void  )
virtual

Reimplemented from elastix::BaseComponent.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimation ( void  )
protectedvirtual

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

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimationOriginal ( void  )
protectedvirtual

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

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimationUsingDisplacementDistribution ( void  )
protectedvirtual

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

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeEachResolution ( void  )
virtual

Reimplemented from elastix::BaseComponent.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeRegistration ( void  )
virtual

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

Reimplemented from elastix::BaseComponent.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::CheckForAdvancedTransform ( void  )
protectedvirtual

Check if the transform is an advanced transform. Called by Initialize.

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")

template<class TElastix >
virtual bool elastix::AdaptiveStochasticGradientDescent< TElastix >::GetAutomaticParameterEstimation ( ) const
virtual
template<class TElastix >
virtual const char* elastix::AdaptiveStochasticGradientDescent< TElastix >::GetClassName ( ) const
virtual

Run-time type information (and related methods).

Reimplemented from itk::AdaptiveStochasticGradientDescentOptimizer.

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

Get the MaximumNumberOfSamplingAttempts.

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

template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::itkStaticConstMacro ( FixedImageDimension  ,
unsigned  int,
FixedImageType::ImageDimension   
)
protected
template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::itkStaticConstMacro ( MovingImageDimension  ,
unsigned  int,
MovingImageType::ImageDimension   
)
protected
template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::MetricErrorResponse ( itk::ExceptionObject &  err)
virtual

Stop optimization and pass on exception.

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

Method for creation through the object factory.

template<class TElastix >
void elastix::AdaptiveStochasticGradientDescent< TElastix >::operator= ( const Self )
private
template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::PrintSettingsVector ( const SettingsVectorType settings) const
protectedvirtual

Print the contents of the settings vector to elxout.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::ResumeOptimization ( void  )
virtual

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

Reimplemented from itk::GradientDescentOptimizer2.

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.

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.

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

Set the MaximumNumberOfSamplingAttempts.

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

Set/Get maximum step length.

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::StartOptimization ( void  )
virtual

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

Reimplemented from itk::StandardGradientDescentOptimizer.

Field Documentation

template<class TElastix >
AdvancedTransformPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AdvancedTransform
protected

The transform stored as AdvancedTransform

Definition at line 352 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimation
private

Definition at line 408 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimationDone
private

Definition at line 415 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_CurrentNumberOfSamplingAttempts
private

Definition at line 413 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaxBandCovSize
private

Private variables for band size estimation of covariance matrix.

Definition at line 418 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumNumberOfSamplingAttempts
private

Private variables for the sampling attempts.

Definition at line 412 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLength
private

Definition at line 409 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfBandStructureSamples
private

Definition at line 419 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfGradientMeasurements
protected

Some options for automatic parameter estimation.

Definition at line 347 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfJacobianMeasurements
protected

Definition at line 348 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfSamplesForExactGradient
protected

Definition at line 349 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_OriginalButSigmoidToDefault
private

Definition at line 423 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_PreviousErrorAtIteration
private

Definition at line 414 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
RandomGeneratorPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_RandomGenerator
protected

RandomGenerator for AddRandomPerturbation.

Definition at line 355 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
SettingsVectorType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SettingsVector
protected

Variable to store the automatically determined settings for each resolution.

Definition at line 344 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SigmoidScaleFactor
protected

Definition at line 357 of file elxAdaptiveStochasticGradientDescent.h.

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_UseNoiseCompensation
private

The flag of using noise compensation.

Definition at line 422 of file elxAdaptiveStochasticGradientDescent.h.



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