ITK  4.1.0
Insight Segmentation and Registration Toolkit
Public Types | Public Member Functions | Static Public Member Functions | Protected Member Functions | Private Types | Private Member Functions | Private Attributes | Static Private Attributes
itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage > Class Template Reference

#include <itkImageGaussianModelEstimator.h>

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

List of all members.

Public Types

typedef SmartPointer< const SelfConstPointer
typedef ImageRegionIterator
< TInputImage > 
InputImageIterator
typedef TInputImage::PixelType InputImagePixelType
typedef TInputImage::Pointer InputImagePointer
typedef
TMembershipFunction::Pointer 
MembershipFunctionPointer
typedef TMembershipFunction MembershipFunctionType
typedef SmartPointer< SelfPointer
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

MatrixTypem_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.


Member Typedef Documentation

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef SmartPointer< const Self > itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::ConstPointer
template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef ImageRegionIterator< TInputImage > itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::InputImageIterator

Type definitions for the iterators for the input and training images.

Definition at line 108 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TInputImage::PixelType itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::InputImagePixelType

Type definition for the vector associated with input image pixel type.

Definition at line 101 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TInputImage::Pointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::InputImagePointer

Type definition for the input image.

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

Definition at line 91 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TInputImage::SizeType itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::InputImageSizeType [private]

Definition at line 135 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef vnl_matrix< double > itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::MatrixType [private]

Definition at line 133 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TMembershipFunction::Pointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::MembershipFunctionPointer

Type definitions for the membership function .

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

Definition at line 113 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TMembershipFunction itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::MembershipFunctionType

Type definitions for the membership function .

Definition at line 112 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef SmartPointer< Self > itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::Pointer
template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef ImageGaussianModelEstimator itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::Self

Standard class typedefs.

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

Definition at line 82 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef ImageModelEstimatorBase< TInputImage, TMembershipFunction > itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::Superclass
template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef ImageRegionIterator< TTrainingImage > itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::TrainingImageIterator

Definition at line 109 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TTrainingImage::PixelType itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::TrainingImagePixelType

Type definitions for the vector holding training image pixel type.

Definition at line 105 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
typedef TTrainingImage::Pointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::TrainingImagePointer

Type definitions for the training image.

Definition at line 97 of file itkImageGaussianModelEstimator.h.


Constructor & Destructor Documentation

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::ImageGaussianModelEstimator ( ) [protected]

Get the training image.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::~ImageGaussianModelEstimator ( ) [protected]

Get the training image.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::ImageGaussianModelEstimator ( const Self ) [private]

Member Function Documentation

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
virtual::itk::LightObject::Pointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::CreateAnother ( void  ) const [virtual]

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::LightProcessObject.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
void itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::EstimateGaussianModelParameters ( ) [private]
template<class TInputImage , class TMembershipFunction , class TTrainingImage >
virtual void itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::EstimateModels ( ) [private, virtual]

A function that generates the model based on the training input data. Achieves the goal of training the classifier.

Implements itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
void itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::GenerateData ( ) [protected, virtual]

Starts the image modelling process

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
virtual const char* itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::GetNameOfClass ( ) const [virtual]

Run-time type information (and related methods).

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
virtual TrainingImagePointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::GetTrainingImage ( ) const [virtual]

Get the training image.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
static Pointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::New ( ) [static]

Method for creation through the object factory.

Reimplemented from itk::LightProcessObject.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
void itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::operator= ( const Self ) [private]

This method causes the filter to generate its output.

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
virtual void itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::PrintSelf ( std::ostream &  os,
Indent  indent 
) const [protected, virtual]

Get the training image.

Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
virtual void itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::SetTrainingImage ( TrainingImagePointer  _arg) [virtual]

Set the training image.


Member Data Documentation

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
MatrixType* itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::m_Covariance [private]

Definition at line 143 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
MatrixType itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::m_Means [private]

Definition at line 142 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
MatrixType itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::m_NumberOfSamples [private]

Definition at line 141 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
TrainingImagePointer itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::m_TrainingImage [private]

Definition at line 145 of file itkImageGaussianModelEstimator.h.

template<class TInputImage , class TMembershipFunction , class TTrainingImage >
const unsigned int itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >::VectorDimension = InputImagePixelType::Dimension [static, private]

Dimension of each individual pixel vector.

Definition at line 139 of file itkImageGaussianModelEstimator.h.


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