Segmentation and tracking of moving cells in time-lapse images is an important problem in biomedical image analysis. For Myxococcus xanthus, rod-like cells with highly coordinated motion, their segmentation and tracking are challenging because cells may touch tightly and form dense swarms that are difficult to identify accurately. Common methods fall under two frameworks: detection association and model evolution. Each framework has its own advantages and disadvantages. In this paper, we propose a new hybrid framework combining these two frameworks into one and leveraging their complementary advantages. Also, we propose an active contour model based on the Ribbon Snake and Chan-Vese model, which is seamlessly integrated with our hybrid framework. Our approach outperforms the state-of-the-art cell tracking algorithms on identifying completed cell trajectories, and achieves higher segmentation accuracy than some best known cell segmentation algorithms.
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