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

Authors:
Seyed Hamid Rezatofighi;Stephen Gould;Ba-Ngu Vo;Katarina Mele;William E. Hughes;Richard Hartley
Affiliations:
College of Engineering & Computer Sci., Australian National University, ACT, Australia,CSIRO Math., Informatics & Statistics, Quantitative Imaging Group, NSW, Australia;College of Engineering & Computer Sci., Australian National University, ACT, Australia;Department of Electrical and Computer Engineering, Curtin University, WA, Australia;CSIRO Math., Informatics & Statistics, Quantitative Imaging Group, NSW, Australia;The Garvan Institute of Medical Research, NSW, Australia,Department of Medicine, St. Vincent's Hospital, NSW, Australia;College of Engineering & Computer Sci., Australian National University, ACT, Australia,National ICT (NICTA), Australia
Venue:
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Year:
2013

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Abstract

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.