Title: Tracking multiple interacting subcellular structure by sequential Monte Carlo method

Authors: Quan Wen, Kate Luby-Phelps, Jean Gao

Addresses: 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

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.

Keywords: subcellular structure tracking; SMC; sequential Monte Carlo; RJMCMC; reversible jump Markov chain Monte Carlo; bioinformatics; green fluorescent proteins; GFP; multiple interacting object tracking; image sequences.

DOI: 10.1504/IJDMB.2009.026704

International Journal of Data Mining and Bioinformatics, 2009 Vol.3 No.3, pp.314 - 332

Published online: 23 Jun 2009 *

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