Abstract
The multi-target tracking in cell image sequences is the main difficulty in cells’ locomotion study. Aim to study cells’ complexity movement in high-density cells’ image, this chapter has proposed a system of segmentation and tracking. The proposed tracking algorithm has combined overlapping and topological constraints with track inactive and active cells, respectively. In order to improve performance of algorithm, size factor has been introduced as a new restriction to quantification criterion of similarity based on Zhang’s method. And the distance threshold for transforming segmented image into graph is adjusted on considering the local distribution of cells’ district in one image. The improved algorithm has been tested in two different image sequences, which have high or low contrast ration separately. Experimental results show that our approach has improved tracking accuracy from 3% to 9% compared with Zhang’s algorithm, especially when cells are in high density and cells’ splitting occurred frequently. And the final tracking accuracy can reach 90.24% and 77.08%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gage FH (2000) Mammalian neural stem cell. Science 287:1433–1438
Tang C, Bentsson E (2005) Segmentation and tracking of neural stem cell. ICIC 2005, Hefei, China, LNCS 3645. Germany: Springer, pp. 851–859
Zimmer C, Labruyere E et al (2002) Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Transactions on Medical Imaging 21:1212–1221
Dzyubachyk O, Niessen W et al (2007) A variational model for level-set based cell tracking in time lapse fluorescence microscopy images. 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007, pp. 12–15
Debeir O, Ham P V et al (2005) Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. IEEE Transactions on Medical Imaging 24:697–711
Kirubarajan T, Bar-Shalom Y et al (2001) Multiassignment for tracking a large number of overlapping Objects. IEEE Transactions on Aerospace and Electronic Systems 37:2–19
Zhang L, Xiong H et al (2007) Graph theory application in cell nucleus segmentation, tracking and identification. Proceedings of the 7th IEEE International Conference on BIBE 2007: 226–232
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9:62–66
Kaneko T, Okudaira M (1985) Encoding of arbitrary curves based on the chain code representation. IEEE Transactions on Communications 33:697–707
Diestel R (2008) Graphy theory, 2nd ed. Beijing: Springer
Acknowledgements
The authors would like to thank Dr Xiaobo Zhou in Harvard Medical School, Harvard University, for providing the image sequence I and DSS of Chalmers University of Technology for recording the stem cell image sequences II. This project is supported by National Natural Science Foundation of China (NSFC). Grant number: 60875020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Tang, C., Ma, L., Xu, D. (2011). Topological Constraint in High-Density Cells’ Tracking of Image Sequences. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_25
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7046-6_25
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7045-9
Online ISBN: 978-1-4419-7046-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)