TY - GEN
T1 - Markov chain Monte Carlo data association for merge and split detection in tracking protein clusters
AU - Quan, Wen
AU - Jean, Gao
AU - Luby-Phelps, Kate
PY - 2006
Y1 - 2006
N2 - Tagging and tracking protein molecules with the help of laser scanning confocal microscope (LSCM) are a key to better understanding of proteomics in diverse aspects. One challenge of tracking multiple green fluorescent protein (GFP) clusters is how to deal with the interaction between multiple objects, namely splitting and merging. In this paper, we propose a framework to track multiple GFP clusters merge and split by using Markov chain Monte Carlo data association (MCMCDA) method combined with asymmetric region matching strategy. The experimental results show that the method is promising.
AB - Tagging and tracking protein molecules with the help of laser scanning confocal microscope (LSCM) are a key to better understanding of proteomics in diverse aspects. One challenge of tracking multiple green fluorescent protein (GFP) clusters is how to deal with the interaction between multiple objects, namely splitting and merging. In this paper, we propose a framework to track multiple GFP clusters merge and split by using Markov chain Monte Carlo data association (MCMCDA) method combined with asymmetric region matching strategy. The experimental results show that the method is promising.
UR - http://www.scopus.com/inward/record.url?scp=34047226841&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34047226841&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2006.781
DO - 10.1109/ICPR.2006.781
M3 - Conference contribution
AN - SCOPUS:34047226841
SN - 0769525210
SN - 9780769525211
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1030
EP - 1033
BT - Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006
T2 - 18th International Conference on Pattern Recognition, ICPR 2006
Y2 - 20 August 2006 through 24 August 2006
ER -