The analysis of biological cell behaviors using Bayesian bidirectional network model

As the increasing demand on medical and biological research, automatic behavior analysis for large scale biological cells faces a big challenge. A novel Bayesian bidirectional network model is proposed in this paper for detection and statistic of the cells behaviors such as birth, vanishing, split, merging and so on. First the cells in every frame are distinguished from background by mean shift filter and level set segmentation. Then we build a bidirectional Bayesian network by modeling the cell regions as vertices and the similarity of cell regions in successive frames as directed edges. By applying weights on the directed edges, the graph model represents the relation between cell regions. Finally, in order to remove fake edges, a iterative algorithm is adopted for analyzing the graph model and obtaining the optimal path of each cell. Experiment result shows that the proposed method is effective and accurate to detect most cells behaviors.