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This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.

Inspired by ant's stochastic behavior in search for multiple food sources, we propose a cooperating multi-task ant system for tracking multiple synthetic objects as well as multiple real cells in a bio-medical field. In our framework, each ant colony is assumed and assigned to fulfill a given task to estimate the state of an object. Furthermore, two ant levels are used, i.e., ant individual level and ant cooperation level. In the ant individual level, ants within one colony perform independently, and the motion of each individual is probabilistically determined by both its intended motion modes and the likelihood function score. In the ant cooperation level, each ant adjusts individual state within its influence region according to heuristic information of all other ants within the same colony, while the global best template at current iteration is found among all ant colonies and utilized to update ant model probability, influence region, and probability of fulfilling task. Our algorithm is validated by comparing it to the-state-of-art algorithms, and specifically the improved tracking performance in terms of false negative rate (up to 10.0%) and false negative rate (up to 2.1%) is achieved based on the studied three real cell image sequences. [ABSTRACT FROM AUTHOR]

Abstract: To reliably analyze multi-cell motion in a series of low-contrast image sequences, we present a novel heuristically restrictive ant system, which operates in a non-optimization way, to adaptively estimate multiple parameters of multiple cells. First, the local intensity variation measure on each pixel of image is defined to generate ant colony initial distribution positions, which are further treated as boundary markers to restrict ant searching behavior. Afterwards, to speed up the ant searching process, both location and contour ant decision behaviors are modeled appropriately to acquire cell position and edge estimates on their individual pheromone fields, which are formed by restrictive pheromone deposits but operate independently and in parallel. Finally, the stability of our proposed pheromone control mechanism is proven to guarantee reliable multi-parameter extraction. Experiment results show that our algorithm could automatically and accurately track numerous cells in various scenarios, and it shows considerable robustness against other popular tracking methods. [Copyright &y& Elsevier]