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Multiple Hypothesis Tracking for Cluttered Biological Image Sequences
Nov. 2013 (vol. 35 no. 11)
pp. 2736-3750
N. Chenouard, Quantitative Image Anal. Unit, Inst. Pasteur, Paris, France
I. Bloch, LTCI, Telecom ParisTech, Paris, France
J. Olivo-Marin, Quantitative Image Anal. Unit, Inst. Pasteur, Paris, France
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.
Index Terms:
probability,biology computing,feature extraction,image sequences,object tracking,Bayesian tracking technique,biological image sequence,unified probabilistic framework,biological particle tracking,microscope image,particle motion model,fluorescence image feature,track extraction process,multiframe approach,multiple hypothesis tracking algorithm,bioimaging,Target tracking,Radar tracking,Bayes methods,Computational modeling,Biological system modeling,cluttered images,Particle tracking,biological imaging,multiple hypothesis tracking,target perceivability
Citation:
N. Chenouard, I. Bloch, J. Olivo-Marin, "Multiple Hypothesis Tracking for Cluttered Biological Image Sequences," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2736-3750, Nov. 2013, doi:10.1109/TPAMI.2013.97
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