OBJECTIVE CELL IMAGE ANALYSIS

Abstract

A system of computer programs processes digitized images of cells. The gray value arrays are considered as a two-dimensionally extended stochastic process. Supervised learning is employed to estimate the parameters of this process in different cell types, to derive classification algorithms and to compute a likelihood for correct classification for each cell. These were found to range from 10:1 to 106:1 for tumor cells from the female genital tract. A data bank for tumor cells has been established. Nonsupervised learning algorithms are used to examine sets of cells for homogeneity. Synthesized cell images of known stochastic properties can be generated to test the completeness of the derived classification rules. Under development are programs defining the changes observable in images of a given cell type during a disease process, by the deformation of the covariance matrix of image properties.