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Automated tracking approach with ant colonies for different cell population density distribution

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Abstract

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.

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Acknowledgments

This work is supported by national natural science foundation of China (No. 61273312), the natural science fundamental research program of higher education colleges in Jiangsu province (No. 14KJB510001), Suzhou Municipal Science and Technology Plan Project (No. SYG201548) and the project of talent peak of six industries (DZXX-013).

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Authors and Affiliations

  1. School of Electrical and Automatic Engineering, Changshu Institute of Technology, 215500, Changshu, China

    Mingli Lu, Benlian Xu, Peiyi Zhu & Jian Shi

  2. School of Automation, Nanjing University of Science and Technology, 210094, Nanjing, China

    Mingli Lu & Andong Sheng

  3. Department of Biomedical Engineering, University of Houston, Houston, 77004, USA

    Zhengqiang Jiang

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  1. Mingli Lu
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  2. Benlian Xu
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  3. Zhengqiang Jiang
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  4. Andong Sheng
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  5. Peiyi Zhu
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  6. Jian Shi
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Correspondence to Benlian Xu.

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The authors do not have any possible conflicts of interest.

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Communicated by V. Loia.

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Lu, M., Xu, B., Jiang, Z. et al. Automated tracking approach with ant colonies for different cell population density distribution. Soft Comput 21, 3977–3992 (2017). https://doi.org/10.1007/s00500-016-2048-7

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