Advertisement

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)

Abstract

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.

Keywords

Multimodality image fusion non-rigid registration fuzzy classification variational model 

References

  1. 1.
    Fitton, I., et al.: Semi-automatic delineation using weighted ct-mri registered images for radiotherapy of nasopharyngeal cancer. The International Journal of Medical Physics Research and Practice 38, 4662–4666 (2011)Google Scholar
  2. 2.
    Bourouis, S., Hamrouni, K.: 3d segmentation of mri brain using level set and unsupervised classification. International Journal in Image and Graphics (IJIG) 10(1), 135–154 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Seim, H., et al.: Automatic segmentation of the pelvic bones from ct data based on a statistical shape model. In: Eurographics Workshop on Visual Computing for Biomedicine, pp. 224–230 (2008)Google Scholar
  4. 4.
    Vincent, G., Wolstenholme, C., Scott, I., Bowes, M.: Fully automatic segmentation of the knee joint using active appearance models. In: Proc. Medical Image Analysis for the Clinic (2010)Google Scholar
  5. 5.
    Jrme Schmid, N.M.T.: Fully automatic segmentation of the knee joint using active appearance models. Med. Image Comput. Comput. Assist. Interv. 11, 119–126 (2008)Google Scholar
  6. 6.
    Frangi, A., et al.: Bone tumor segmentation from mr perfusion images with neural networks using multi-scale pharmacokinetic features. In: Image and Vision Computing, pp. 679–690 (2001)Google Scholar
  7. 7.
    Myronenko, A., Song, X.: Intensity-based image registration by minimizing residual complexity. IEEE Trans. on Medical Imaging 29, 1882–1891 (2010)CrossRefGoogle Scholar
  8. 8.
    Myronenko, A., Song, X., Carreira-perpinán, M.A.: Free-form nonrigid image registration using generalized elastic nets. In: IEEE Conf. of Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  9. 9.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)CrossRefGoogle Scholar
  11. 11.
    Pal, N.R., Pal, S.K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems 13(4), 517–530 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Bourouis, S., Hamrouni, K.: Fully automatic brain tumor segmentation based on multi-modality mri and level-set. Journal of Ubiquitous Systems and Pervasive Networks 3(2), 47–54 (2011)CrossRefGoogle Scholar
  13. 13.
    Sethian, J.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd edn. Cambridge University Press (1999)Google Scholar

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

Personalised recommendations