Automatic Image Segmentation Using a Deformable Model Based on Charged Particles

  • Andrei C. Jalba
  • Michael H. F. Wilkinson
  • Jos B. T. M. Roerdink
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)


We present a method for automatic segmentation of grey-scale images, based on a recently introduced deformable model, the charged-particle model (CPM). The model is inspired by classical electrodynamics and is based on a simulation of charged particles moving in an electrostatic field. The charges are attracted towards the contours of the objects of interest by an electrostatic field, whose sources are computed based on the gradient-magnitude image. Unlike the case of active contours, extensive user interaction in the initialization phase is not mandatory, and segmentation can be performed automatically. To demonstrate the reliability of the model, we conducted experiments on a large database of microscopic images of diatom shells. Since the shells are highly textured, a post-processing step is necessary in order to extract only their outlines.


Segmentation Result Active Contour Automatic Segmentation Coulomb Force Deformable Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Jalba, A.C., Wilkinson, M.H.F., Roerdink, J.B.T.M.: CPM: A deformable model for shape recovery and segmentation based on charged particles. IEEE Trans. Pattern Anal. Machine Intell. (2004) (in press)Google Scholar
  2. 2.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1, 321–331 (1987)CrossRefGoogle Scholar
  3. 3.
    Jalba, A.C., Roerdink, J.B.T.M.: Automatic segmentation of diatom images. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 369–376. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge Univ. Press, Cambridge (1988)zbMATHGoogle Scholar
  5. 5.
    Amenta, N., Bern, M., Eppstein, D.: The crust and the β-skeleton: Combinatorial curve reconstruction. Graphical Models and Image Processing 60, 125–135 (1998)CrossRefGoogle Scholar
  6. 6.
    Wilkinson, M.H.F., Jalba, A.C., Urbach, E.R., Roerdink, J.B.T.M.: Identification by mathematical morphology. In: DuBuf, J.M.H., Bayer, M.M. (eds.) Automatic Diatom Identification. Series in Machine Perception and Artificial Intelligence, vol. 51, pp. 221–244. World Scientific Publishing Co., Singapore (2002)CrossRefGoogle Scholar
  7. 7.
    Pech-Pacheco, J.L., Cristobal, G.: Automatic slide scanning. In: du Buf, H., Bayer, M.M. (eds.) Automatic Diatom Identification, pp. 259–288. World Scientific Publishing, Singapore (2002)CrossRefGoogle Scholar
  8. 8.
    Fischer, S., Bunke, H., Shahbazkia, H.R.: Contour extraction. In: du Buf, H., Bayer, M. (eds.) Automatic Diatom Identification, pp. 93–107. World Scientific Publishing, Singapore (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Andrei C. Jalba
    • 1
  • Michael H. F. Wilkinson
    • 1
  • Jos B. T. M. Roerdink
    • 1
  1. 1.Institute of Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands

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