Automated Matching of Genomic Structures in Microscopic Images of Living Cells Using an Information Theoretic Approach

Advances in microscopic imaging technology have revolutionized biology in recent years by enabling the study of dynamic processes inside living cells. Time-lapse microscopy produces large numbers of sequential images of living cells taken over time. In this paper we describe the novel approaches we have developed to automate and introduce high accuracy to the process of identifying genomic structures in living cells and matching them between consecutive time-points. We derive control points from landmarks within the structures and use the Kulback-Leibler divergence as an information-theoretic approach to correctly resolve potential close matches within the iterative closest point (ICP) algorithm. We also describe the steps needed to extend our techniques to analyze three dimensional voxel images. The approaches we describe are widely applicable in the analysis of time-lapse microscopy data