A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences

Seyed Hamid Rezatofighi, Stephen Gould, Ba-Ngu Vo, Katarina Mele, William E. Hughes, Richard Hartley

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

16 Citations (Scopus)


Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings
Number of pages13
ISBN (Print)9783642388675
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference on Information Processing in Medical Imaging 2013: IPMI 2013 - Asilomar, United States of America
Duration: 28 Jun 20133 Jul 2013
Conference number: 23rd

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Information Processing in Medical Imaging 2013
CountryUnited States of America

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