Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering

Ihor Smal, Erik Meijering, K Draegestein, Niels Galjart, I Grigoriev, Anna Akhmanova, Martin van Royen, Adriaan Houtsmuller, Wiro Niessen

Research output: Contribution to journalArticleAcademic

76 Citations (Scopus)


Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions. (C) 2008 Elsevier B.V. All rights reserved.
Original languageUndefined/Unknown
Pages (from-to)764-777
Number of pages14
JournalMedical Image Analysis
Issue number6
Publication statusPublished - 2008

Research programs

  • EMC MGC-02-13-02
  • EMC MM-03-24-01
  • EMC NIHES-03-30-03

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