Abstract
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.
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References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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© 2012 Springer-Verlag Berlin Heidelberg
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Zhu, C., Guan, Q., Chen, S. (2012). A Novel Cell Segmentation, Tracking and Dynamic Analysis Method in Time-Lapse Microscopy Based on Cell Local Graph Structure and Motion Features. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_45
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DOI: https://doi.org/10.1007/978-3-642-33506-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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