Image Segmentation Through Dual Pyramid of Agents

  • K. Idir
  • H. Merouani
  • Y. Tlili
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


An effective method for the early detection of breast cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multi-agent system based on a dual irregular pyramid.

An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence.


Dual Pyramid Image Segmentation Multi-agent System Region/Edge Cooperation 


  1. 1.
    Bertolino, P.: Contributions des pyramides irrégulières en segmentation d’images multirésolution. Ph.D. thesis, Institut National Polytechnique de Grenoble (1995)Google Scholar
  2. 2.
    Boucher. Une approche décentralisée et adaptative de la gestion d’informations en vision, Application à l’interprétation d’images de cellules en mouvement. Ph.D. thesis, Université Joseph Fourier, Grenoble (1999)Google Scholar
  3. 3.
    Canny, J.F.: A computational approach to edge detection. Patt Ana. Mach. Int 8(6), 679–698 (1986)CrossRefGoogle Scholar
  4. 4.
    DDSM: Digital Database for Screening Mammography. University of South Florida,
  5. 5.
    Duchesnay, E.: Agents situés dans l’image et organisés en pyramide irrégulière. Contribution à la segmentation par une approche d’agrégation coopérative et adaptative. Ph.D. Université de Rennes-1 (2001)Google Scholar
  6. 6.
    Gaspoz, F.: Mammographie digitale & Analyse d’images par ordinateur. Laboratoire TIMC – IMAG. Faculté de Médecine Grenoble. Université Joseph Fourier Grenoble, France (2003)Google Scholar
  7. 7.
    Germond, L., Dojat, M., Taylor, C., Garbay, C.: A Cooperative Framework for Segmentation of MRI Brain Scans. Artif. Intell. Med. 20, 277–294 (2000)Google Scholar
  8. 8.
    Haxhimusa, Y., Kropatsch, W.G.: Hierarchical Image Partitioning with Dual Graph Contraction. Technical Report PRIP-TR-81, Institute of Computer Aided Automation 183/2, Patt. Recogn. Image. Proc Group, Austria (2003)Google Scholar
  9. 9.
    Jolion, J.M., Montanvert, A.: The adapted pyramid: a framework for 2d image analysis. Computer Vision Graphics and Image Processing 55(3), 339–348 (1992)zbMATHGoogle Scholar
  10. 10.
    Kropatsch, W.G.: Building irregular pyramids by Dual Graph Contraction. Technical Report PRIP-TR-35, Institute of Automation 183/2, Dept. for Patt. Rec. Image. Proc, TU Wien, Austria (1994)Google Scholar
  11. 11.
    Meer, P.: Stochastic image pyramids. Comp. Vision. Graph. Image Proc. 45(3), 269–294 (1989)Google Scholar
  12. 12.
    Settache, H.: Une plate-forme multi-agents pour la segmentation d’images: Application dans le domaine des IRM cérébrales 2D. DEA Report, Université de Caen (2002)Google Scholar
  13. 13.
    Willersinn, D.: Parallel Graph Contraction for Dual Irregular Pyramids. PRIP-TR 28, Institute for Automation, 183/2, Technical University of Vienna, Austria (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • K. Idir
    • 1
  • H. Merouani
    • 1
  • Y. Tlili
    • 1
  1. 1.Laboratory of computer science Research., Pattern Recognition Group, Dept. of computer science – Faculty of engineer scienceBadji Mokhtar UniversityAnnabaAlgeria

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