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Cytological Image Classification Using Data Reduction

In this paper, the authors investigate data reduction techniques using cytological image data for the purpose of further classification applying modern approaches. Cytological images are widely used in diagnosing cancerous and precancerous conditions of the breast. Classification of the whole image is a rather time consuming process, so the authors apply an approach when quantitative characteristics of micro-objects (cell nuclei) are used for classification. The authors carried out a comparative analysis of the classification of the cytological images based on the quantitative characteristics of their nuclei using modern classifiers. The main criteria for describing micro-objects (cell nuclei) are the following ones: area, perimeter, circumference, maximum width and length, area and perimeter of the bounding box. The structure of the biomedical image classification system is developed, including image processing stages, calculations of quantitative characteristics of micro-objects, data reduction and classification. The principal component method is used as data reduction technique. To classify data the following methods are used: a single-layer perceptron, logistic regression, support vectors machine, and the k-nearest neighbor method. Testing was performed using BPCI2100 database of cytological images of cancerous and precancerous conditions of the breast.