Globally Segmentation Using Active Contours and Belief Function

  • Foued Derraz
  • Miloud Boussahla
  • Laurent Peyrodie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


We study the active contours (AC) based globally segmentation for vector-valued image incorporating both statistical and evidential knowledge. The proposed method combine both Belief Functions (BFs) and probability functions in the same framework. In this formulation, all features issued from vector-valued image are integrated in inside/outside descriptors to drive the segmentation process based AC. In this formulation, the imprecision caused by the weak contrast and noise between inside and outside descriptors issued from the multiple channels is controlled by the BFs as weighted parameters. We demonstrated the performance of our segmentation algorithm using some challenging color biomedical images.


Active Contours Characteristic function Belief Function Dempster Shafer rule 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Foued Derraz
    • 1
    • 3
  • Miloud Boussahla
    • 3
  • Laurent Peyrodie
    • 2
  1. 1.Facult Libre de Mdicine, Institut Catholique de LilleUniversit Catholique de LilleFrance
  2. 2.Hautes Etudes d’IngenieurUniversit Catholique de LilleFrance
  3. 3.Telecommunication Laboratory, Technology FacultyAbou Bekr Belkaid UniversityTlemcenAlgeria

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