Markov chain Monte Carlo data association for merge and split detection in tracking protein clusters

Wen Quan, Gao Jean, Kate Luby-Phelps

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages1030-1033
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Quan, W., Jean, G., & Luby-Phelps, K. (2006). Markov chain Monte Carlo data association for merge and split detection in tracking protein clusters. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 1030-1033). [1699064] (Proceedings - International Conference on Pattern Recognition; Vol. 1). https://doi.org/10.1109/ICPR.2006.781