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