Proposals:Refactoring Statistics Framework 2007 Background

The main components of a classification framework are

1. Input
   1. Image
   2. Data points
2. Membership models
   1. Can be manually set or automatically generated from the sample data
   2. Estimators are available to generate membership functions ( ImageModelEstimatorBase, ImageGuassianModelEstimator,ExpectationMaximizationMixtureModelEstimator )
   3. Some classes are named with Estimator suffix but they do more than just estimating membership functions
      1. itkKdTreeBasedKmeansEstimator
3. Distance functions
4. Decision Rules
5. Classifiers

Note:

1. Classifiers provide interface that integrates all the other components. Classifiers provide a common framework
2. ITK also contains classes which combine specific types of the different components into one Huge framework such as itkScalarImageKmeansImageFilter and itkBayesianClassifierImageFilter.
   1. itkScalarImageKmeansImageFilter: EuclideanDistance, KdTreeBasedKmeansEstimator, SampleClassifier, MinimumDecisionRule
   2. itkBayesianClassifierImageFilter: Bayesian Estimator, MaximumDecisionRule