Fast Statistical Level Sets Image Segmentation for Biomedical Applications

  • Sophie Schüpp
  • Abderrahim Elmoataz
  • Mohamed-Jalal Fadili
  • Daniel Bloyet
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)


In medical microscopy, image analysis offers to pathologist a modern tool, which can be applied to several problems in cancerology: quantification of DNA content, quantification of immunostaining, nuclear mitosis counting, characterization of tumor tissue architecture. However, these problems need an accurate and automatic segmentation. In most cases, the segmentation is concerned with the extraction of cell nuclei or cell clusters. In this paper, we address the problem of the fully automatic segmentation of grey level intensity or color images from medical microscopy. An automatic segmentation method combining fuzzy clustering and multiple active contour models is presented. Automatic and fast initialization algorithm based on fuzzy clustering and morphological tools are used to robustly identify and classify all possible seed regions in the color image. These seeds are propagated outward simultaneously to refine contours of all objects. A fast level set formulation is used to model the multiple contour evolution. Our method is illustrated through two representative problems in cytology and histology.


Segmentation active contour models level set method fuzzy clustering, medical microscopy. 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Morel, S. Soleimini, Variational. Methods in Image Segmentation. Progress in Nonlinear Differential Equations Equations and their application, Birkhaüser 1995.Google Scholar
  2. 2.
    V. Caselles, F. Catte, T. Coll, F. Dibos, A geometric model of active contours”, Numerische Mathematick, 66 (1993) 1–3.MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    S. Kichenassamy, A. Kumar, P.J. Olver, A. Tannenbaum, A. Yezzi, Gradient flows and geometric active contours, Proceeding of Fifth International Conference on Computer Vision, Cambridge, (1995) 810–815.Google Scholar
  4. 4.
    C. Samson, L. Blanc-Feraud, G. Aubert, J. Zerubia, A level set model for image classification”, rapport de recherche, INRIA, France, RR-3662 (1999).Google Scholar
  5. 5.
    T. Chan, B.Y. Sandberg, L. Vese, Active Contours without Edges for Vector-Valued Images, J. of Visual Communication and Image Representation, 11 (2000) 130–141.CrossRefGoogle Scholar
  6. 6.
    T. Chan, B.L. Vese, Active Contours and Segmentations Models Using Geometric PDE’s for Medical Imaging,UCLA CAM Report (2000).Google Scholar
  7. 7.
    R. Malladi, B.J.A Sethian, A Real-time Algorithm for Meduical Shape Recovery, Proceeding of International Conference on Computer Vision,Mubai, India (1998) 304–310.Google Scholar
  8. 8.
    A. Sarti, C. Ortiz, S. Lockett, R. Malladi, A Geometric Model for 3D Confocal Microscope Image Analysis, Preprint, Lurence Berkeley National Laboratory, LBLNL-41740, 1999.Google Scholar
  9. 9.
    A. Elmoataz, S. Schüpp, R. Clouard, P. Herlin, D. Bloyet, Using active contours and mathematical morphology tools for quantification of immunohistochemical images, Signal Processing, 71 (1998) 215–226.zbMATHCrossRefGoogle Scholar
  10. 10.
    S. Kim, O(N) Level set, Math-Report, University of Kentuckey, (2000).Google Scholar
  11. 11.
    S. Osher, J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations, Journal of computational physics, 79 (1988) 12–49,.MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    J. Sethian, Level Set Methods: Evolving interfaces in geometry, fluid mechanics, computer vision, and material science. Cambridge University Press, (1996).Google Scholar
  13. 13.
    E. Rouy, A. Tourin, “A viscosity solutions approach to shape-from-shading, SIAMC J. Numer. Anal., (1992) 29 867–884.MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    A. Lorette, X. Descombes, J. Zerubia, Urban areas extraction based on texture analysis through a markovian modelling, International Journal of Computer Vision 36 (2000) 219–234.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Sophie Schüpp
    • 1
  • Abderrahim Elmoataz
    • 1
  • Mohamed-Jalal Fadili
    • 1
  • Daniel Bloyet
    • 1
  1. 1.Groupe de Recherche en InformatiqueImage et Instrumentation de Caen UMR 6072CaenFrance

Personalised recommendations