Multimodal Bone Cancer Detection Using Fuzzy Classification and Variational Model

  • Sami Bourouis
  • Ines Chennoufi
  • Kamel Hamrouni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)


Precise segmentation of bone cancer is an important step for several applications. However, the achievement of this task has proven problematic due to lack of contrast and the non homogeneous intensities in many modalities such as MRI and CT-scans. In this paper we investigate this line of research by introducing a new method for segmenting bone cancer. Our segmentation process involves different steps: a registration step of different image modalities, a fuzzy-possibilistic classification (FPCM) step and a final segmentation step based on a variational model. The registration and the FPCM algorithms are used to locate and to initialize accurately the deformable model that will evolve smoothly to delineate the expected tumor boundaries. Preliminary results show accurate and promising detection of the cancer region.


Multimodality image fusion non-rigid registration fuzzy classification variational model 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sami Bourouis
    • 1
    • 3
  • Ines Chennoufi
    • 2
  • Kamel Hamrouni
    • 1
  1. 1.Ecole Nationale dingnieurs de TunisUniversit de Tunis El ManarTunisia
  2. 2.School of EngineeringESPRITTunisTunisia
  3. 3.Taif UniversityKingdom of Saudi Arabia

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