Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification
A novel cell nucleus tracking approach is raised in this paper, developed from applied graph theory that simultaneously handles major challenges of cells touching, splitting, disappearing and emerging. The key point of our work lies in the graphs with robust structures that constructed from large cells groups. Then the problem of nucleus tracking is simplified to that of vertex matching between graphs generated from successive frames. Single cells are modeled as vertices in the graph and edges are built between neighboring ones, through which cells in large quantities are connected altogether. The algorithm works through a process of graph saturation following vertices with maximal entering belief-propagation flows. Feasibility of our approach is validated by so-called 'Crowds Model' that explains motional features of multi-cells groups. In processing CT image sources with high cell intensities as well as complex variations in structures, robustness of our approach is confirmed in that both successful tracking rates and segmentation rates are kept above 96%.