TY - GEN
T1 - Multiple interacting subcellular structure tracking by sequential Monte Carlo method
AU - Wen, Quan
AU - Gao, Jean
AU - Luby-Phelps, Kate
PY - 2007/12/1
Y1 - 2007/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=49049115527&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49049115527&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2007.28
DO - 10.1109/BIBM.2007.28
M3 - Conference contribution
AN - SCOPUS:49049115527
SN - 0769530311
SN - 9780769530314
T3 - Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
SP - 437
EP - 442
BT - Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
T2 - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
Y2 - 2 November 2007 through 4 November 2007
ER -