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Actin Filament Segmentation Using Spatiotemporal Active-Surface and Active-Contour Models

  • Hongsheng Li
  • Tian Shen
  • Dimitrios Vavylonis
  • Xiaolei Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6361)

Abstract

We introduce a novel algorithm for actin filament segmentation in a 2D TIRFM image sequence. We treat the 2D time-lapse sequence as a 3D image volume and propose an over-grown active surface model to segment the body of a filament on all slices simultaneously. In order to locate the two ends of the filament on the over-grown surface, a novel 2D spatiotemporal domain is created based on the resulting surface. Two 2D active contour models deform in this domain to locate the two filament ends accurately. Evaluation on TIRFM image sequences with very low SNRs and comparison with a previous method demonstrate the accuracy and robustness of this approach.

References

  1. 1.
    Fujiwara, I., Vavylonis, D., Pollard, T.D.: Polymerization kinetics of ADP- and ADP-Pi-actin determined by fluorescence microscopy. Proc. Natl. Acad. Sci. USA 104, 8827–8832 (2007)CrossRefGoogle Scholar
  2. 2.
    Kuhn, J.R., Pollard, T.D.: Real-time measurements of actin filament polymerization by total internal reflection fluorescence microscopy. Biophys. J. 88, 1387–1402 (2005)CrossRefGoogle Scholar
  3. 3.
    Li, H., Shen, T., Smith, M., Fujiwara, I., Vavylonis, D., Huang, X.: Automated actin filament segmentation, tracking and tip elongation measurements based on open active contour models. In: Proc. ISBI (2009)Google Scholar
  4. 4.
    Hadjidemetriou, S., Toomre, D., Duncan, J.: Motion tracking of the outer tips of microtubules. Medical Image Analysis 12, 689–702 (2008)CrossRefGoogle Scholar
  5. 5.
    Saban, M., Altinok, A., Peck, A., Kenney, C., Feinstein, S., Wilson, L., Rose, K., Manjunath, B.: Automated tracking and modeling of microtubule dynamics. In: Proc. ISBI, vol. 1, pp. 1032–1035 (2006)Google Scholar
  6. 6.
    Sargin, M.E., Altinok, A., Kiris, E., Feinstein, S.C., Wilson, L., Rose, K., Manjunath, B.S.: Tracing microtubules in live cell images. In: Proc. ISBI (2007)Google Scholar
  7. 7.
    Smal, I., Draegestein, K., Galjart, N., Niessen, W., Meijering, E.: Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: Application to microtubule growth analysis. IEEE Trans. on Medical Imaging 27, 789–804 (2008)CrossRefGoogle Scholar
  8. 8.
    Kong, K., Marcus, A., Giannakakou, P., Wang, M.: Using particle filter to track and model microtubule dynamics. In: Proc. ICIP, vol. 5, pp. 517–520 (2007)Google Scholar
  9. 9.
    Li, H., Shen, T., Vavylonis, D., Huang, X.: Actin filament tracking based on particle filters and stretching open active contour models. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 673–681. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    Cohen, L.D., Cohen, I.: Finite element methods for active contour models and balloons for 2d and 3d images. IEEE Trans. on Pattern Analysis and Machine Intelligence 15, 1131–1147 (1991)CrossRefGoogle Scholar
  11. 11.
    Besl, P., McKay, H.: A method for registration of 3-D shapes. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 239–256 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hongsheng Li
    • 1
  • Tian Shen
    • 1
  • Dimitrios Vavylonis
    • 2
  • Xiaolei Huang
    • 1
  1. 1.Department of Computer Science & EngineeringLehigh UniversityUSA
  2. 2.Department of PhysicsLehigh UniversityUSA

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