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

  • Chen Zhu
  • Qiu Guan
  • Shengyong Chen
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


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.


Touching cells Local Graph structure Cell dynamic analysis Time-lapse microscopy Cell tracking 


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  1. 1.
    Wang, Q., Niemi, J., Tan, C.M., You, L., West, M.: Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy. Cytometry Part A 70A, 101–110 (2010)Google Scholar
  2. 2.
    Richard, M.J., Danny, C., Nie, L.: Live-cell tracking using SIFT Features in DIC Microscopic Videos. IEEE Transactions on Biomedical Engineering 57, 2219–2227 (2010)CrossRefGoogle Scholar
  3. 3.
    Akberdewan, M.A., Ahmad, M.O.: Tracking biological cells in Time-lapse Microscopy: An adaptive technique combining motion and topological features. IEEE Transcations on Biomedical Engineering 58, 1637–1647 (2001)Google Scholar
  4. 4.
    Kanade, T., Yin, Z.Z., Bise, R., Huh, S., Eom, S.: Cell image analysis: Algorithms, System and Applications. Applications of Computer Vision, 374–381 (2011)Google Scholar
  5. 5.
    Debeir, O., Van Hum, P., Kiss, R., Decaestecker, C.: Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. IEEE Transactions on Medical Imaging 24, 697–711 (2005)CrossRefGoogle Scholar
  6. 6.
    Zimmer, C., Labruyere, E., M-Yedid, V., Guillen, N., O-Marin, J.C.: Segmentation and tracking of migrating cells in video microscopy with parametric active contours: a tool for cell-based drug testing. IEEE Transactions on Medical Imaging 21, 1212–1221 (2002)CrossRefGoogle Scholar
  7. 7.
    Padfield, D., Rittscher, J., Thomas, N., Roysam, B.: Spatio-temporal cell cycle phase analysis using level sets and fast marching methods. Medical Image Analysis 13, 143–155 (2009)CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Xiong, H., Zhang, K., Zhou, X.: Graph theory application in cell nucleus segmentation, tracking and identification. In: Proceeding of the 7th IEEE International Conference on BIBE, pp. 226–232 (2007)Google Scholar
  9. 9.
    Chowdhury, A.S., Chatterjee, R., Ghosh, M., Ray, N.: Cell Cracking In Video Microscopy Using Bipartite Graph Matching. In: IEEE International Conference on Pattern Recognition, ICPR, pp. 2456–2459 (2010)Google Scholar
  10. 10.
    Malpica, N., de Solorzano, C.O., Vaquero, J.J., Santos, A.S., Vallcorba, I., Garcia-Sagredo, J.M., de Poze, F.: Applying Watershed Algorithms to the Segmentation of Clustered Nuclei. Cytometry 28, 289–297 (1997)CrossRefGoogle Scholar
  11. 11.
    Liu, M., Roy-Chowdhury, A., Gonehal, V.R.: Exploiting local structure for tracking plant cells in noisy images. In: IEEE International Conference on Image Processing, ICIP, pp. 1765–1768 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chen Zhu
    • 1
  • Qiu Guan
    • 1
  • Shengyong Chen
    • 1
  1. 1.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina

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