Gaussian mixture and Markov models for cell-phase classification in microscopic imaging

Abstract

Studies of drug effects on cancer cells are performed through measuring cell cycle progression such as inter phase, prophase, metaphase and anaphase in individual cells. Such studies require the processing and analysis of huge amounts of image data. Manual image analysis is very time consuming thus costly, potentially inaccurate and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and tracking of individual cells in a dynamic cellular population. Image classification of cell phases in a fully automatic manner presents the most difficult task of such analysis. We considered applying several versions of Gaussian mixture and Markov models for automating the classification of cell nuclei in different mitotic phases recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance.