Multiple Hypothesis Tracking for Cluttered Biological Image Sequences

• Published in: IEEE transactions on pattern analysis and machine intelligence 2013-11, Vol.35 (11), p.2736-3750
• Main Authors: Chenouard, N., Bloch, I., Olivo-Marin, J.
• Format: Article
• Language: English
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial Intelligence; Bayes methods; Biological and medical sciences; biological imaging; Biological system modeling; Biosensors; Biotechnology; cluttered images; Computational modeling; Computer Science; Computer science; control theory; systems; Computer Simulation; Data Interpretation, Statistical; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image Processing; Methods. Procedures. Technologies; Models, Statistical; multiple hypothesis tracking; Particle tracking; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Radar tracking; Subtraction Technique; target perceivability; Target tracking; Theoretical computing; Various methods and equipments
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2013.97

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.