#include <itkImageGaussianModelEstimator.h>

Inheritance diagram for itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >:

Collaboration diagram for itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >:

Public Types
using	ConstPointer = SmartPointer< const Self >

using	InputImageConstIterator = ImageRegionConstIterator< TInputImage >

using	InputImageConstPointer = typename TInputImage::ConstPointer

using	InputImageIterator = ImageRegionIterator< TInputImage >

using	InputImagePixelType = typename TInputImage::PixelType

using	InputImagePointer = typename TInputImage::Pointer

using	InputImageType = TInputImage

using	MembershipFunctionPointer = typename TMembershipFunction::Pointer

using	MembershipFunctionType = TMembershipFunction

using	Pointer = SmartPointer< Self >

using	Self = ImageGaussianModelEstimator

using	Superclass = ImageModelEstimatorBase< TInputImage, TMembershipFunction >

using	TrainingImageConstIterator = ImageRegionConstIterator< TTrainingImage >

using	TrainingImageConstPointer = typename TTrainingImage::ConstPointer

using	TrainingImageIterator = ImageRegionIterator< TTrainingImage >

using	TrainingImagePixelType = typename TTrainingImage::PixelType

using	TrainingImagePointer = typename TTrainingImage::Pointer

using	TrainingImageType = TTrainingImage

Public Types inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
using	ConstPointer = SmartPointer< const Self >

using	InputImagePointer = typename TInputImage::Pointer

using	InputImageType = TInputImage

using	MembershipFunctionPointer = typename TMembershipFunction::Pointer

using	MembershipFunctionPointerVector = std::vector< MembershipFunctionPointer >

using	Pointer = SmartPointer< Self >

using	Self = ImageModelEstimatorBase

using	Superclass = LightProcessObject

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

using	Pointer = SmartPointer< Self >

using	Self = LightProcessObject

using	Superclass = Object

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

using	Pointer = SmartPointer< Self >

using	Self = Object

using	Superclass = LightObject

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

using	Pointer = SmartPointer< Self >

using	Self = LightObject

Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother () const

virtual const char *	GetNameOfClass () const

Public Member Functions inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
virtual const unsigned int &	GetNumberOfModels () const

virtual void	SetNumberOfModels (unsigned int _arg)

virtual void	SetInputImage (InputImageType *_arg)

virtual InputImageType *	GetModifiableInputImage ()

virtual const InputImageType *	GetInputImage () const

void	SetMembershipFunctions (MembershipFunctionPointerVector membershipFunctions)

const MembershipFunctionPointerVector	GetMembershipFunctions () const

unsigned int	GetNumberOfMembershipFunctions ()

void	DeleteAllMembershipFunctions ()

unsigned int	AddMembershipFunction (MembershipFunctionPointer function)

void	Update ()

Public Member Functions inherited from itk::LightProcessObject
virtual void	AbortGenerateDataOff ()

virtual void	AbortGenerateDataOn ()

virtual const bool &	GetAbortGenerateData () const

virtual void	SetAbortGenerateData (bool _arg)

virtual void	SetProgress (float _arg)

virtual const float &	GetProgress () const

void	UpdateProgress (float amount)

virtual void	UpdateOutputData ()

Public Member Functions inherited from itk::Object
unsigned long	AddObserver (const EventObject &event, Command *)

unsigned long	AddObserver (const EventObject &event, Command *) const

unsigned long	AddObserver (const EventObject &event, std::function< void(const EventObject &)> function) const

virtual void	DebugOff () const

virtual void	DebugOn () const

Command *	GetCommand (unsigned long tag)

bool	GetDebug () const

MetaDataDictionary &	GetMetaDataDictionary ()

const MetaDataDictionary &	GetMetaDataDictionary () const

virtual ModifiedTimeType	GetMTime () const

virtual const TimeStamp &	GetTimeStamp () const

bool	HasObserver (const EventObject &event) const

void	InvokeEvent (const EventObject &)

void	InvokeEvent (const EventObject &) const

virtual void	Modified () const

void	Register () const override

void	RemoveAllObservers ()

void	RemoveObserver (unsigned long tag)

void	SetDebug (bool debugFlag) const

void	SetReferenceCount (int) override

void	UnRegister () const noexcept override

void	SetMetaDataDictionary (const MetaDataDictionary &rhs)

void	SetMetaDataDictionary (MetaDataDictionary &&rrhs)

virtual void	SetObjectName (std::string _arg)

virtual const std::string &	GetObjectName () const

Public Member Functions inherited from itk::LightObject
Pointer	Clone () const

virtual void	Delete ()

virtual int	GetReferenceCount () const

void	Print (std::ostream &os, Indent indent=0) const

Static Public Member Functions
static Pointer	New ()

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

Static Public Member Functions inherited from itk::Object
static bool	GetGlobalWarningDisplay ()

static void	GlobalWarningDisplayOff ()

static void	GlobalWarningDisplayOn ()

static Pointer	New ()

static void	SetGlobalWarningDisplay (bool val)

Static Public Member Functions inherited from itk::LightObject
static void	BreakOnError ()

static Pointer	New ()

using	MatrixType = vnl_matrix< double >

using	InputImageSizeType = typename TInputImage::SizeType

static constexpr unsigned int	VectorDimension = InputImagePixelType::Dimension

MatrixType	m_NumberOfSamples

MatrixType	m_Means

MatrixType *	m_Covariance { nullptr }

TrainingImagePointer	m_TrainingImage

virtual void	SetTrainingImage (TrainingImageType *_arg)

virtual TrainingImageType *	GetModifiableTrainingImage ()

virtual const TrainingImageType *	GetTrainingImage () const

	ImageGaussianModelEstimator ()=default

	~ImageGaussianModelEstimator () override

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

void	GenerateData () override

void	EstimateModels () override

void	EstimateGaussianModelParameters ()

Additional Inherited Members
Protected Member Functions inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
	ImageModelEstimatorBase ()

	~ImageModelEstimatorBase () override=default

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

void	GenerateData () override

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

	~LightProcessObject () override

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

	~Object () override

bool	PrintObservers (std::ostream &os, Indent indent) const

virtual void	SetTimeStamp (const TimeStamp &timeStamp)

Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer	InternalClone () const

	LightObject ()

virtual void	PrintHeader (std::ostream &os, Indent indent) const

virtual void	PrintTrailer (std::ostream &os, Indent indent) const

virtual	~LightObject ()

Protected Attributes inherited from itk::LightObject
std::atomic< int >	m_ReferenceCount

Detailed Description

template<typename TInputImage, typename TMembershipFunction, typename TTrainingImage>
class itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >

Base class for ImageGaussianModelEstimator object.

itkImageGaussianModelEstimator generates the Gaussian model for given tissue types (or class types) in an input training data set for segmentation. The training data set is typically provided as a set of labelled/classified data set by the user. A Gaussian model is generated for each label present in the training data set.

The user should ensure that both the input and training images are of the same size. The input data consists of the raw data and the training data has class labels associated with each pixel.

A zero label is used to identify the background. A model is not calculated for the background (its mean and covariance will be zero). Positive labels are classes for which models will be estimated. Negative labels indicate unlabeled data where no models will be estimated.

This object supports data handling of multiband images. The object accepts the input image in vector format only, where each pixel is a vector and each element of the vector corresponds to an entry from 1 particular band of a multiband dataset. A single band image is treated as a vector image with a single element for every vector. The classified image is treated as a single band scalar image.

This function is templated over the type of input and output images. In addition, a third parameter for the MembershipFunction needs to be specified. In this case a Membership function that stores Gaussian models needs to be specified.

The function EstimateModels() calculates the various models, creates the membership function objects and populates them.

Examples: Examples/Segmentation/GibbsPriorImageFilter1.cxx.

Definition at line 76 of file itkImageGaussianModelEstimator.h.