Automated classification of cytological specimens based on features extracted from nuclei images

摘要

The paper describes the pattern recognition subsystem of an automated specimen analyser being developed for pre-screening applications with a high resolution system. The complete recognition system is divided into a hierarchy of two different recognition systems: the single cell classifier, and the specimen classifier. The single cell image classifier operates on the cell data of isolated cell images based on features which are extracted from the nucleus only. For each specimen a large number of single cells are processed by the single cell classifier. The collection of the resulting discriminant vectors-and not just the decisions only-are used as measurements for the subsequent specimen classifier, which has to produce a real-valued discriminant function indicating the degree of suspiciousness of the specimen analysed. By proper thresholding of this figure of malignancy, the final decision can be made. The approach offers the opportunity of detecting suspicious cell modifications in an early stage.