The classification of specimens is an important issue in automated cytometry. Whereas cytometry started as a collection of techniques to classify single objects (cells), making use of classical pattern recognition procedures, more recently it has widened its scope with the development and application of techniques for the classification of cell samples (specimens). Various approaches which address specific aspects of the problem have been mentioned in the literature. This paper presents a rather comprehensive model which combines features of earlier approaches. The model allows both between- and within-specimen variability of the features and is applicable to a cell-class approach as well as a cell-feature approach. Also, it allows the optimal design of a specimen- and cell-classifier cascade.

A Model for the Classification of Cytological Specimens

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A Model for the Classification of Cytological Specimens