Paper Info
Title
Multiple hypothesis tracking for cluttered biological image sequences.
Abstract
In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.
Year
DOI
Venue
2013
10.1109/TPAMI.2013.97
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
Field
DocType
biological particle,proposed framework,biological image sequence,advanced bayesian tracking technique,unified probabilistic framework,dynamical biological process,poor imaging condition,state-of-the-art tracking technique,accurate computational modeling,tracking procedure,cluttered biological image sequences,multiple hypothesis tracking,object tracking,bioinformatics,computational modeling,biomedical research,text mining,probability,radar tracking,feature extraction
Computer vision,Radar tracker,Pattern recognition,Computer science,Feature (computer vision),Feature extraction,Robustness (computer science),Video tracking,Biological imaging,Artificial intelligence,Randomness,Bayesian probability
Journal
Volume
Issue
ISSN
35
11
1939-3539
Citations 
PageRank 
References 
35
1.70
17
Authors
3
Name
Order
Citations
PageRank
Nicolas Chenouard113411.04
Isabelle Bloch22123170.75
Jean-Christophe Olivo-Marin374777.94