Joint Tracking of Cell Morphology and Motion

  • Jierong Cheng
  • Esther G. L. Koh
  • Sohail Ahmed
  • Jagath C. Rajapakse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

A new method is proposed for joint tracking of cell morphology and motion from 3D temporal cellular images. We adopt the framework of region-based active contours for segmentation, which is able to cope with objects having blurred boundaries. Motion estimation is performed by optical flow to increase the robustness and accuracy. Cell morphology and motion are modelled via a unified energy formulation and estimated iteratively searching for the minimum energy configuration. Experiments are carried out on synthetic and real cellular images to demonstrate the performance of the method.

Keywords

Cell segmentation tracking motion estimation optical flow active contours level sets 

References

  1. 1.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. International Journal of Computer Vision 1(4), 321–331 (1987)CrossRefGoogle Scholar
  2. 2.
    Meegama, R.G.N., Rajapakse, J.C.: Nurbs snakes. Image and Vision Computing 21, 551–562 (2003)CrossRefGoogle Scholar
  3. 3.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266–277 (2001)CrossRefGoogle Scholar
  4. 4.
    Zhang, B., Zimmer, C., Olivo-Marin, J.-C.: Tracking fluorescent cells with coupled geometric active contours. In: Proc. IEEE Int’l Symp. Biomedical Imaging (ISBI), pp. 476–479 (2004)Google Scholar
  5. 5.
    Cheng, J., Rajapakse, J.C.: Segmentation of clustered nuclei with shape markers and marking function. IEEE Trans. Biomedical Engineering 53(3) (2009)Google Scholar
  6. 6.
    Yu, W., Lee, H.K., Hariharan, S., Bu, W., Ahmed, S.: Quantitative neurite outgrowth measurement based on image segmentation with topological dependence. Cytometry Part A 75A(4), 289–297 (2008)CrossRefGoogle Scholar
  7. 7.
    Dufour, A., Shinin, V., Tajbakhsh, S., Guillen, N., Olivo-Marin, J.-C., Zimmer, C.: Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans. Image Processing 14(9), 1396–1410 (2005)CrossRefGoogle Scholar
  8. 8.
    Althoff, K., Degerman, J., Wählby, C., Thorlin, T., Faijerson, J., Eriksson, P.S., Gustavsson, T.: Time-lapse microscopy and classification of in vitro cell migration using hidden markov modeling. In: Proc. IEEE Int’l Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 5, pp. 1165–1168 (2006)Google Scholar
  9. 9.
    Sage, D., Neumann, F.R., Hediger, F., Gasser, S.M., Unser, M.: Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics. IEEE Trans. Image Processing 14(9), 1372–1383 (2005)CrossRefGoogle Scholar
  10. 10.
    Yang, X., Li, H., Zhou, X.: Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy. IEEE Trans. Circ. Sys. -I 53(11), 2405–2414 (2006)CrossRefGoogle Scholar
  11. 11.
    Melani, C., Campana, M., Lombardot, B., Rizzi, B., Veronesi, F., Zanella, C., Bourgine, P., Mikula, K., Peyrieras, N., Sarti, A.: Cells tracking in a live zebrafish embryo. In: Proc. 29th Annual International Conference of IEEE Engineering in Medicine and Biology Society, pp. 1631–1634 (2007)Google Scholar
  12. 12.
    Olivo-Marin, J.-C.: An overview of image analysis in multidimensional biological microscopy. In: Proc. IEEE Int’l Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 5, pp. 1173–1176 (2006)Google Scholar
  13. 13.
    Mitiche, A., Feghali, R., Mansouri, A.: Motion tracking as spatio-temporal motion boundary detection. Robotics and Autonomous Systems 43, 39–50 (2003)CrossRefGoogle Scholar
  14. 14.
    Feghali, R.: Multi-frame simultaneous motion estmation and segmentation. IEEE Trans. Consumer Electronics 51(1), 245–248 (2005)CrossRefGoogle Scholar
  15. 15.
    Mitiche, A., Sekkati, H.: Optical flow 3d segmentation and interpretation: A variational method with active curve evolution and level sets. IEEE Trans. Pattern Anal. Machine Intell. 28(11), 1818–1829 (2006)CrossRefGoogle Scholar
  16. 16.
    Sekkati, H., Mitiche, A.: Joint optical flow estimation, segmentation, and 3d interpretation with level sets. Computer Vision and Image Understanding 103(2), 89–154 (2006)CrossRefGoogle Scholar
  17. 17.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 23, 185–203 (1981)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jierong Cheng
    • 1
    • 2
  • Esther G. L. Koh
    • 3
  • Sohail Ahmed
    • 3
  • Jagath C. Rajapakse
    • 1
    • 2
    • 4
  1. 1.BioInformatics Research Center and School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Singapore-MIT AllianceSingapore
  3. 3.Institute of Medical BiologySingapore
  4. 4.Department of Biological EngineeringMassachusetts Institute of TechnologyUSA

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