RJMCMC-based tracking of vesicles in fluorescence time-lapse microscopy

David C Nam, Kenton Arkill, Richard Eales, Lorna Hodgson, Paul Verkade, Alin Achim

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
219 Downloads (Pure)

Abstract

Vesicles are a key component for the transport of materials throughout the cell. To manually analyze the behaviors of vesicles in fluorescence time-lapse microscopy images would be almost impossible. This is also true for the identification of key events, such as merging and splitting. In order to automate and increase the reliability of this processes we introduce a Reversible Jump Markov chain Monte Carlo method for tracking vesicles and identifying merging/splitting events, based on object interactions. We evaluate our method on a series of synthetic videos with varying degrees of noise. We show that our method compares well with other state-of-the-art techniques and well-known microscopy tracking tools. The robustness of our method is also demonstrated on real microscopy videos.
Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference (EUSIPCO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2801-2805
Number of pages5
ISBN (Electronic)9780992862633
ISBN (Print)9781479988518
DOIs
Publication statusPublished - Mar 2016
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: 31 Aug 20154 Sep 2015

Publication series

NameProceedings of the European Signal Processing Conference (EUSIPCO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)2219-5491

Conference

Conference23rd European Signal Processing Conference, EUSIPCO 2015
CountryFrance
CityNice
Period31/08/154/09/15

Keywords

  • biomedical imaging
  • Light microscopy
  • MCMC
  • merging
  • splitting

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