Multiple interacting subcellular structure tracking by sequential Monte Carlo method

With the wide application of green fluorescent protein (GFP) in the study of live cells, there is a surging need for the computer-aided analysis on the huge amount of image sequence data acquired by the advanced microscopy devices. One of such tasks is the motility analysis of the multiple subcellular structures. In this paper, an algorithm using sequential Monte Carlo (SMC) method for multiple interacting object tracking is proposed. First, marker residual image is applied to detect individual subcellular structure automatically, and to represent all the objects together using the joint state. Then the interaction between objects in the 2D plane is modeled by augmenting an extra dimension and evaluating the overlapping relationship in the 3D space. Finally, the distribution of the dimension varying joint state is sampled efficiently by Reversible jump Markov chain Monte Carlo (RJMCMC) algorithm with a novel height swap move. The experimental results show that our method is promising.