ITK  4.3.0
Insight Segmentation and Registration Toolkit
Classes
Module ITKClassifiers
Group Segmentation
+ Collaboration diagram for Module ITKClassifiers:

Classes

class  itk::BayesianClassifierImageFilter< TInputVectorImage, TLabelsType, TPosteriorsPrecisionType, TPriorsPrecisionType >
 Performs Bayesian Classification on an image. More...
 
class  itk::BayesianClassifierInitializationImageFilter< TInputImage, TProbabilityPrecisionType >
 This filter is intended to be used as a helper class to initialize the BayesianClassifierImageFilter. More...
 
class  itk::ClassifierBase< TDataContainer >
 Base class for classifier objects. More...
 
class  itk::ImageClassifierBase< TInputImage, TClassifiedImage >
 Base class for the ImageClassifierBase object. More...
 
class  itk::ImageGaussianModelEstimator< TInputImage, TMembershipFunction, TTrainingImage >
 Base class for ImageGaussianModelEstimator object. More...
 
class  itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >
 Base class for ImageKmeansModelEstimator object. More...
 
class  itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
 Base class for model estimation from images used for classification. More...
 
class  itk::ScalarImageKmeansImageFilter< TInputImage, TOutputImage >
 Classifies the intensity values of a scalar image using the K-Means algorithm. More...
 

Detailed Description

This module contains algorithms to classify pixels in an image. It can be used, for example, to identify pixel membership within a set of tissue types. Different algorithms are available including Bayesian classification, Gaussian models, and K-means clustering. After tissue labels have been assigned, they can be modified and applied with the Module ITKLabelMap.

Dependencies: