#include <itkImageGaussianModelEstimator.h>

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

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

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef ImageRegionIterator < TInputImage >	InputImageIterator
typedef TInputImage::PixelType	InputImagePixelType
typedef TInputImage::Pointer	InputImagePointer
typedef TMembershipFunction::Pointer	MembershipFunctionPointer
typedef TMembershipFunction	MembershipFunctionType
typedef SmartPointer< Self >	Pointer
typedef ImageGaussianModelEstimator	Self
typedef ImageModelEstimatorBase < TInputImage, TMembershipFunction >	Superclass
typedef ImageRegionIterator < TTrainingImage >	TrainingImageIterator
typedef TTrainingImage::PixelType	TrainingImagePixelType
typedef TTrainingImage::Pointer	TrainingImagePointer
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual const char *	GetNameOfClass () const
virtual void	SetTrainingImage (TrainingImagePointer _arg)
Static Public Member Functions
static Pointer	New ()
Protected Member Functions
void	GenerateData ()
Private Types
typedef TInputImage::SizeType	InputImageSizeType
typedef vnl_matrix< double >	MatrixType
Private Member Functions
void	EstimateGaussianModelParameters ()
virtual void	EstimateModels ()
	ImageGaussianModelEstimator (const Self &)
void	operator= (const Self &)
Private Attributes
MatrixType *	m_Covariance
MatrixType	m_Means
MatrixType	m_NumberOfSamples
TrainingImagePointer	m_TrainingImage
Static Private Attributes
static const unsigned int	VectorDimension = InputImagePixelType::Dimension
virtual TrainingImagePointer	GetTrainingImage () const
	ImageGaussianModelEstimator ()
	~ImageGaussianModelEstimator ()
virtual void	PrintSelf (std::ostream &os, Indent indent) const

Detailed Description

template<class TInputImage, class TMembershipFunction, class 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 calcualted 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.

Definition at line 77 of file itkImageGaussianModelEstimator.h.