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An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.

The rapid progress in computer technology makes possible the automatic analysis of thousands of cells on a slide in the field of automatic uterine cancer cytology. Our approach starts with the high-resolution scanning of visually selected and classified single cells determining the training set for discriminant analysis. On the basis of more than 15 morphologic and textural features measured, correct classification results of 95% are reached. Our aim is to speed up our image processing system by means of an Array-Processor, the model AP 120 B from Floating Point Systems. In this study a realistic time estimation of the scanning, segmentation, feature extraction and classification of about 40,000 cells on a slide is performed.

Machine recognition of ectocervical cells has been achieved with good classification success by a number of research groups. Even though a substantial number of cell image features have been introduced, only a moderate number are required for classification. For them, definitions and measures of their discriminatory potential are presented.

Two methods for high resolution cell image data acquisition are applied routinely. Cells are either scanned by a computer controlled fast scanning microscope photometer (SMP) or a TV-camera. The software system for digital image analysis was completely revised and implemented on the PR 330 minicomputer. The system contains codes for primary cell data acquisition, segmentation of cells, cell feature extraction and statistical cell analysis. With this system, SMP and TV scanned cell data bases of PAP stained cells in vaginal smears, grouped into several classes, have been built up. Each data base contains 34 primary features and 20 feature combinations for each cell. A linear discriminant analysis is applied routinely for cell classification. The present state of the system and its operation are described, cell features and classification results are shown, and future steps for a prescreening strategy are discussed.