Tracking multiple interacting subcellular structure by sequential Monte Carlo method

Authors:
Quan Wen;Kate Luby-Phelps;Jean Gao
Affiliations:
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.;Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.;Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2009

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Abstract

With the wide application of Green Fluorescent Proteins (GFP) in the study of live cells, there is a surging need for computer-aided analysis on the huge amount of image sequence data acquired by the advanced microscopy devices. In this paper, a framework based on Sequential Monte Carlo (SMC) is proposed for multiple interacting object tracking. The distribution of the dimension varying joint state is sampled efficiently by a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm with a novel height swap move. Experimental results were performed on synthetic and real confocal microscopy image sequences.