An ant-based stochastic searching behavior parameter estimate algorithm for multiple cells tracking

This paper presents a novel ant-based parameter estimate algorithm to accurately track multiple cells in a series of low-contrast image sequences. Our proposed algorithm consists of three main blocks, i.e., priori colony distribution block, multi-colony reconstruction block, and cell labeling and state extraction block. Priori colony distribution block aims to directly distribute birth ants into regions where cells probably occur, which is implemented through kernel density probability estimate. Multi-colony reconstruction block is to move ants towards potential regions based on histogram similarity and place agent pheromone with appropriate introduction to evaporation and propagation models. Cell labeling and state extraction block is implemented by a fast ant clustering algorithm to determine the number of cells and their individual states, and the ratio of known identity ants to unknown ants in a cluster contributes to discriminate cell identity. Experiment results show that our algorithm could automatically track numerous cells in various scenarios, and furthermore, it is more accurate and robust than other popular tracking methods.