A Novel Cell Segmentation, Tracking and Dynamic Analysis Method in Time-Lapse Microscopy Based on Cell Local Graph Structure and Motion Features

In this paper, a novel cell segmentation, tracking and dynamic analysis vision-based method is proposed,which can be used to analyze cell population morphology and dynamic change of the cell sequence images obtained by time-lapse-microscopy. Firstly, in process of the segmentation, a new method is introduced to identify touching cells based on the relative position of the same cell region between the adjacent frames. Secondly, a novel cell tracking method, which combines cell local graph structure with motion features, is also presented to track the fast moving cell population and to improve the cell tracking accuracy. Experiment results show that this proposed method can be used to segment the touching cells correctly and has an increase of 10.66% and 5.74% tracking accuracy compared with the two traditional methods. Furthermore, the dynamic analysis results can be further used for biological researches and applications.

[1]  Nilanjan Ray,et al.  Cell Tracking in Video Microscopy Using Bipartite Graph Matching , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  Amit K. Roy-Chowdhury,et al.  Exploiting local structure for tracking plant cells in noisy images , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[3]  Danny Crookes,et al.  Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos , 2010, IEEE Transactions on Biomedical Engineering.

[4]  Philippe Van Ham,et al.  Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes , 2005, IEEE Transactions on Medical Imaging.

[5]  Xiaobo Zhou,et al.  Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[6]  M. West,et al.  Image segmentation and dynamic lineage analysis in single‐cell fluorescence microscopy , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[7]  Takeo Kanade,et al.  Cell image analysis: Algorithms, system and applications , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[8]  M. Omair Ahmad,et al.  Tracking Biological Cells in Time-Lapse Microscopy: An Adaptive Technique Combining Motion and Topological Features , 2011, IEEE Transactions on Biomedical Engineering.

[9]  Vannary Meas-Yedid,et al.  Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing , 2002, IEEE Transactions on Medical Imaging.

[10]  J J Vaquero,et al.  Applying watershed algorithms to the segmentation of clustered nuclei. , 1998, Cytometry.

[11]  Jens Rittscher,et al.  Spatio-temporal cell cycle phase analysis using level sets and fast marching methods , 2009, Medical Image Anal..