Research On Cell Sequence Tracking Based On Image Segmentation

Object segmentation and tracking based on image or video is a hotspot in the field of computer vision research.The technology is widely used in intelligent video surveillance,astronomical observation,robotics,defense systems,biomedical,industrial testing and other fields.In order to achieve long-term observation and quantitative analysis of cell objects,provide accurate and reliable experimental data for cell kinematics,in our paper,the moving object tracking technique is applied to track the cell object.At present,the cell tracking algorithm is mainly based on the establishment of the cell segmentation results with a high degree of accuracy.Therefore,when error occurs in the cell segmentation module,the accuracy of cell tracking will be seriously affected.This paper deals with the ubiquitous problem of poor tracking accuracy due to errors in cell segmentation.Based on the study of cell segmentation and tracking technology,a combination of randomized Hough transform method and GVF-Snake method is proposed in this paper to establish a step-by-step refinement of cell image.As in this method the problem of cell-to-cell topological relations is studied by using the graph-based object tracking method,the cell tracking problem when segmenting the image is transformed into the minimum cost maximum flow problem.The main research contents include:(1)Establish a step-by-step refinement of the cell image segmentation framework.From cell images preprocessing based on the application of mathematical morphology,the rough contours of the cellular images were obtained by using the random Hough transform,and the accurate contours were extracted by GVF-Snake model.According to experiment,it is shown that the method proposed in this paper effectively improves the accuracy of cell segmentation.(2)Research on cell tracking based on graph theory.With representing the element of the cell sets in the image sequence as nodes and the similarity between the cells in the neighborhood as weights,the graph model is established.With this graph model,a graph reduction method based on topology constraint is proposed.The successive shortest path algorithm is used to solve the minimum cost maximum flow problem.At the same time,the global optimal solution is obtained by the method of marker deletion,and the trajectories of all the cells are found.Finally,the object tracking is achieved.The experimental results show that the method has good tracking effect when testing on cell image sequence.