Tracking multiple interacting subcellular structure by sequential Monte Carlo method

Quan Wen, Kate Luby-Phelps, Jean Gao

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)314-332
Number of pages19
JournalInternational Journal of Data Mining and Bioinformatics
Volume3
Issue number3
DOIs
StatePublished - 2009

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

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