Moving Object Segmentation Using the Flux Tensor for Biological Video Microscopy

  • Kannappan Palaniappan
  • Ilker Ersoy
  • Sumit K. Nath
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4810)

Abstract

Time lapse video microscopy routinely produces terabyte sized biological image sequence collections, especially in high throughput environments, for unraveling cellular mechanisms, screening biomarkers, drug discovery, image-based bioinformatics, etc. Quantitative movement analysis of tissues, cells, organelles or molecules is one of the fundamental signals of biological importance. The accurate detection and segmentation of moving biological objects that are similar but non-homogeneous is the focus of this paper. The problem domain shares similarities with multimedia video analytics. The grayscale structure tensor fails to disambiguate between stationary and moving features without computing dense velocity fields (i.e. optical flow). In this paper we propose a novel motion detection algorithm based on the flux tensor combined with multi-feature level set-based segmentation, using an efficient additive operator splitting (AOS) numerical implementation, that robustly handles deformable motion of non-homogeneous objects. The flux tensor level set framework effectively handles biological video segmentation in the presence of complex biological processes, background noise and clutter.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Eggert, U.S., Mitchison, T.J.: Small molecule screening by imaging. Curr. Opin. Chem. Biol. 10, 232–237 (2006)CrossRefGoogle Scholar
  2. 2.
    Zhou, X., Wong, S.: High content cellular imaging for drug development. IEEE Signal Processing Magazine 23, 170–174 (2006)CrossRefGoogle Scholar
  3. 3.
    Nath, S., Palaniappan, K., Bunyak, F.: Four-color level set segmentation using generalized Voronoi neighborhoods for cell migration. Medical Image Analysis (2007)Google Scholar
  4. 4.
    Davis, P.J., Kosmacek, E.A., Sun, Y., Ianzine, F., Mackey, M.A.: The large scale digital cell analysis system. J. Microscopy (in press, 2007)Google Scholar
  5. 5.
    Palaniappan, K., Jiang, H., Baskin, T.: Non-rigid motion estimation using the robust tensor method. In: IEEE Comp. Vision. Patt. Recog. Workshop on Articulated and Nonrigid Motion, Washington DC, USA, pp. 25–32 (2004)Google Scholar
  6. 6.
    Nath, S., Palaniappan, K., Bunyak, F.: Cell segmentation using coupled level sets and graph-vertex coloring. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 101–108. Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Bunyak, F., Palaniappan, K., Nath, S., Baskin, T., Dong, G.: Quantitative cell motility for in vitro wound healing using level set-based active contour tracking. In: ISBI. Proc. 3rd IEEE Int. Symp. Biomed. Imaging, pp. 1040–1043 (2006)Google Scholar
  8. 8.
    Nath, S., Bunyak, F., Palaniappan, K.: Robust tracking of migrating cells using four-color level set segmentation. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 920–932. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Nath, S., Palaniappan, K., Bunyak, F.: Accurate spatial neighborhood relationships for arbitrarily-shaped objects using Hamilton-Jacobi GVD. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 421–431 (2007)Google Scholar
  10. 10.
    Bunyak, F., Palaniappan, K., Nath, S., Seetharaman, G.: Fux tensor constrained geodesic active contours with sensor fusion for persistent object tracking. J. Multimedia (in Press, 2007)Google Scholar
  11. 11.
    Weele, C., Jiang, H., et al.: A new algorithm for computational image analysis of deformable motion at high spatial and temporal resolution applied to root growth. Plant. Phys. 132, 1138–1148 (2003)CrossRefGoogle Scholar
  12. 12.
    Shenoy, V., Tambe, D., Prasad, A., Theriot, J.: A kinematic description of the trajectories of listeria monocytogenes propelled by actin comet tails. In: Proc. Natl. Acad. Sci., USA, vol. 104, pp. 8229–8234 (2007)Google Scholar
  13. 13.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: Integrating color, texture, motion, shape. Intern. J. Computer Vis. 72, 195–215 (2007)CrossRefGoogle Scholar
  14. 14.
    Jeon, M., Alexander, M., Pedrycz, W., Pizzi, N.: Unsupervised hierarchical image segmentation with level set and additive operator splitting. Patt. Recog. Letters 26, 1461–1469 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kannappan Palaniappan
    • 1
  • Ilker Ersoy
    • 1
  • Sumit K. Nath
    • 1
  1. 1.Department of Computer Science, University of Missouri-Columbia, Columbia MO 65211USA

Personalised recommendations