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The dynamic analysis of cell behavior is fundamental to the evaluation of the correlation between disease and abnormal cell migration. In this study, a self-regulated foraging behavior for cell tracking is proposed under the framework of dual prediction and update of an ant colony and its pheromone field. In the regulated behavior of ant foraging, three strategies are employed: range of foraging, re-sampling, based on the initial distribution of the ant colony; and the stopping criteria of the foraging. The foraging movement of an ant colony is confined to its relevant range determined by the corresponding pheromone field and dynamically varies over iterations. An initial distribution of the ant colonies at each iteration is generated by a Gaussian based re-sampling strategy to regulate an effective search in a centralized manner as a colony. An adaptive stopping criterion of foraging is put forward on the basis of Kullback-Leibler divergence of two approximately Gaussian pheromone fields between two consecutive iterations. The experimental results of the application to cell tracking show that the effectiveness of the algorithm, and demonstrate that it is better than the compared methods. [ABSTRACT FROM AUTHOR]

To encompass irregular cell movement and mitotic events, we present a novel cell track-to-track association approach that rebuilds lineage trees through the pheromone field of a proposed ant colony optimization. With the constraint of maximum inter-frame displacement, the algorithm can link potential tracks by minimizing the proposed cost function considering both cell motion and morphology that mainly occurs on the fragmented intervals. Two different decisions are defined for ant colonies to predict mitotic and non-mitotic events used to construct relevant trail pheromone fields. A novel subsequent processing technique including threshold processing, trail merging and identity fusion is ultimately proposed, that makes full use of the spatial information of the pheromone field to realize track-to-track association. The proposed method has proven to be feasible and reliable on several challenging datasets. [ABSTRACT FROM AUTHOR]

Visual tracking of cells has many challenges due to complex situations such as varying cell population densities, intricate motion patterns and sophisticated interactions with other cells. This paper focuses on an efficient and effective ant-based method with working modes updated to track multiple cells over varying densities in the presence of occlusion or clustering situations. To overcome these challenges, the proposed Ant Colony Optimization (ACO) algorithm models two types of ant working modes, namely, cooperation mode and interactive competition mode, whereas the classical ACO algorithms use only one type of mode which performs poor in clutter situations. Moreover, mode update strategies based on ant colony pheromone is used to adjust pheromone field to obtain accurate state vector of cells. Experimental results demonstrate that the proposed method robustly tracks multiple cells in various scenarios. The averaged LSR, LTR and FTR of our method can be only 1.43, 1.71 and 1.37 %, respectively. Our experimental results also show that our tracking method is competitive with state-of-the-art multi-cell tracking methods. [ABSTRACT FROM AUTHOR]

In terms of the varying number of cell population, shape deformation, collision and uneven movement, a novel method based on multi-task particle swarm optimization (PSO) algorithm without explicit detection module, named MTPSO tracking method, is developed for automatic tracking of biological cells in time-lapse low-contrast microscopy image sequences. For tracking existing cells from the previous frames, a PSO-based tracking module is firstly implemented to give the initial positions of existing cells according to the previous estimated state of each cell, then a PSO-based contour module is proposed to determine the corresponding contour of each cell and finally achieve a precise position tracking by an iterative centroid updating process. For tracking new appearing cells at the current frame, a PSO-based discovery module, followed by the aforementioned PSO-based contour module, is proposed to search for new potential cells through appropriate initialization of particle swarm and searching mechanism. MTPSO tracking method is tested over a number of different real cell image sequences and is shown to provide high accuracy both in position and contour estimate of each cell in various challenging cases. Furthermore, it is more competitive against the state-of-the-art multi-object tracking methods in terms of performance measures such as FAR, FNR, LTR, and LSR. [ABSTRACT FROM AUTHOR]

The problem of multi-cell tracking plays an important role in studying dynamic cell cycle behaviors. In this paper, a novel ant system with multiple tasks is modeled for jointly estimating the number of cells and individual states in cell image sequences. In our ant system, in addition to pure cooperative mechanism used in traditional ant colony optimization algorithm, we model and investigate another two types of ant working modes, namely, dual competitive mode and interactive mode with cooperation and competition to evaluate the tracking performance on spatially adjacent cells. For adjacent ant colonies, dual competitive mode encourages ant colonies with different tasks to work independently, whereas the interactive mode introduces a trade-off between cooperation and competition. In simulations of real cell image sequences, the multi-tasking ant system integrated with interactive mode yielded better tracking results than systems adopting pure cooperation or dual competition alone, both of which cause tracking failures by under-estimating and over-estimating the number of cells, respectively. Furthermore, the results suggest that our algorithm can automatically and accurately track numerous cells in various scenarios, and is competitive with state-of-the-art multi-cell tracking methods. [ABSTRACT FROM AUTHOR]

In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization (ACO), then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter (IMMEKF) for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time. [ABSTRACT FROM AUTHOR]

According to the biased angles provided by the bistatic sensors, the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed, respectively. Additionally, a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally, Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true, and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore, the estimated results can achieve their Cramer-Rao low bounds. [ABSTRACT FROM AUTHOR]

Much research mainly focuses on the batch processing method (e.g. maximum likelihood method) when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]