Cell Segmentation Using Level Set Methods with a New Variance Term

  • Zuzana BílkováEmail author
  • Jindřich Soukup
  • Václav Kučera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)


We present a new method for segmentation of phase-contrast microscopic images of cells. The algorithm is based on the variational formulation of the level set method, i.e. minimizing of a functional, which describes the level set function. The functional is minimized by a gradient flow described by an evolutionary partial differential equation. The most significant new ideas are initialization using thresholding and the introduction of a new term based on local variance that speeds up convergence and achieves more accurate results. The proposed algorithm is applied on real data and compared with another algorithm. Our method yields an average gain in accuracy of 2 %.


Segmentation Level set method Active contours Phase contrast microscopy 



The study was supported by the GAUK grant No. 914813/2013, the grant GAČR No. 13-29225S, the grant SVV-2015-260223 and the grant SVV-2016-260332. The authors would also like to thank the staff of the Working Place of Tissue Culture - Certified Laboratory at Nové Hrady for their assistance with the manual segmentation of the cells.


  1. 1.
    Li, F., Zhou, X., Zhao, H., Wong, S.T.C.: Cell segmentation using front vector flow guided active contours. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 609–616. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Soukup, J., Císař, P., Šroubek, F.: Segmentation of time-lapse images with focus on microscopic images of cells. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 71–80. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Birkbeck, N., Sofka, M., Kohlberger, T., Zhang, J., Wetzl, J., Kaftan, J., Zhou, S.K.: Robust segmentation of challenging lungs in CT using multi-stage learning and level set optimization. In: Suzuki, K. (ed.) Computational Intelligence in Biomedical Imaging, pp. 185–208. Springer, New York (2014)CrossRefGoogle Scholar
  4. 4.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  5. 5.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Li, C., Chenyang, X., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 430–436. IEEE (2005)Google Scholar
  7. 7.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)CrossRefzbMATHGoogle Scholar
  8. 8.
    Vemuri, B., Chen, Y.: Joint Image Registration and Segmentation. Geometric level set methods in imaging, vision, and graphics, pp. 251–269. Springer, New York (2003)Google Scholar
  9. 9.
    Conn, A.R., Gould, N.I., Toint, P.L.: Trust Region Methods, vol. 1. SIAM, Philadelphia (2000)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zuzana Bílková
    • 1
    • 2
    Email author
  • Jindřich Soukup
    • 1
    • 2
  • Václav Kučera
    • 1
  1. 1.Faculty of Mathematics and PhysicsCharles University in PraguePragueCzech Republic
  2. 2.Institute of Information Theory an Automation of the ASCRPragueCzech Republic

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