CFP last date
22 April 2024
Reseach Article

Segmentation of MR Brain Images using a Data Fusion Approach

by Lamiche Chaabane, Moussaoui Abdelouahab
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 36 - Number 12
Year of Publication: 2011
Authors: Lamiche Chaabane, Moussaoui Abdelouahab
10.5120/4553-6450

Lamiche Chaabane, Moussaoui Abdelouahab . Segmentation of MR Brain Images using a Data Fusion Approach. International Journal of Computer Applications. 36, 12 ( December 2011), 27-32. DOI=10.5120/4553-6450

@article{ 10.5120/4553-6450,
author = { Lamiche Chaabane, Moussaoui Abdelouahab },
title = { Segmentation of MR Brain Images using a Data Fusion Approach },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 36 },
number = { 12 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume36/number12/4553-6450/ },
doi = { 10.5120/4553-6450 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:23:03.368157+05:30
%A Lamiche Chaabane
%A Moussaoui Abdelouahab
%T Segmentation of MR Brain Images using a Data Fusion Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 36
%N 12
%P 27-32
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this work is to evaluate the segmentation of MR images using the multispectral fusion approach in the possibility theory context. The process of fusion consists of three steps : (1) information extraction, (2) information combination, and (3) decision step. Information provided by T2-weighted and PD-weighted images is extracted and modeled separately in each one using fuzzy logic, fuzzy maps obtained are combined with an operator which can managing the uncertainty and ambiguity in the images and the final segmented image is constructed in decision step. Some results are presented and discussed.

References
  1. Gonzalez, R. C. and Woods, R. E. 1992. Digital Image Processing, Addison-Wesley.
  2. Bloch, I. 1996. Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recognition Letters 17, 905-919.
  3. Hata, Y., Kobashi, S., and Hirano, S. 2000. Automated segmentation of human brain mr images aided by fuzzy information granulation and fuzzy inference. IEEE Trans. SMC 30, 381-395.
  4. Goldberg-Zimring, D., Achiron, A., and Miron, S. 1998. Automated detection and characterization of multiple sclerosis lesions in brain mr images. Magnetic Resonance Imaging 16, 311-318.
  5. Van Leemput, K., Maes, F., Vandermeulen, D., and Suetens, P. 1999. Automated model-based tissue classification of mr images of the brain. IEEE Trans. Medical Imaging 18, 897-908.
  6. Wang, Y., Adali, T., Xuan, J., and Szabo, Z. 2001. Magnetic resonance image analysis by information theoretic criteria and stochastic models. IEEE Trans. Information Technology in Biomedicine 5, 150-158.
  7. Bloch, I., and Maitre, H. 1997. Data fusion in 2D and 3D image processing: an overview. In proceedings of the X Brazilian symposium on computer graphics and image processing, Brazil, 127-134.
  8. Dou, W., Ruan, S., Chen, Y., Bloyet, D., and Constans, J. M. 2007. A framwork of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image and Vision Computing 25, 164-171.
  9. Barra, V. and Boire, J. Y. 2001. A general framework for the fusion of anatomical and functional medical images. NeuroImage 13, 410-424.
  10. Maria, D. C., Valdés, H., Karen, J. F., Francesca, M. C., and Joanna, M. W. 2010. New multispectral mri data fusion technique for white matter lesion segmentation: method and comparison with thresholding in flair images. Eur Radiol 20, 1684-1691.
  11. Waltz, E. D. 1995. The principals and practice of image and spatial data fusion. In Proceedings of the Eight National Data Fusion Conference. Dalls, 257–278.
  12. Behloul, F., Janier, M., Croisille, P., Poirier, C., Boudraa, A., Unterreiner R., Mason J. C., and Revel D. 1998. Automatic assessment of myocardial viability based on pet-mri data fusion. In Proceedings of the 20th Ann. Int. Conf. IEEE 1, 492-495.
  13. Aguilar, M. and Joshua, R. 2002. New fusion of multi-modality volumetric medical imagery. ISIF, 1206-1212.
  14. Lefevre, E., Vannoorenberghe, P., and Colot, O. 2000. About the use of Dempster-Shafer theory for color image segmentation. In First International Conference on Color in Graphics and Image Processing, Saint-Etienne, France.
  15. Barra, V. and Boire, J. Y. 2001. Automatic segmentation of subcortical brain structures in MR images using information fusion. IEEE Trans. on Med. Imaging. 20, 549-558.
  16. Clarck, M. C., Hall, L. O., Goldgof, D. B., Velthuizen, R, Murtagh, F. R., and Silbiger, M. S. 1998. Automatic tumor segmentation using knowledge-based techniques. IEEE Trans. Med. Imaging 17, 187-201.
  17. Bloch, I. 2000. Fusion of numerical and structural image information in medical imaging in the framework of fuzzy sets. In Fuzzy Systems in Medicine, P. Szczepaniak et al.
  18. Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8, 338-353.
  19. Zadeh, L. A. 1978. Fuzzy sets as a basic for a theory of possibility. Int. Jour. of Fuzzy Sets and Systems 1, 3-28.
  20. Dubois, D. and Prade, H. 1980. Fuzzy Sets and Systems: Theory and Application. Academic Press, New-York.
  21. Dubois, D. and Prade, H. 1985. A Review of Fuzzy Set Aggregation Connectives. Information Sciences 36, 85-121.
  22. Yager, R. R. 1991. Connectives and Quantifiers in Fuzzy Sets. Int. Jour. of Fuzzy sets and systems 40, 39-75.
  23. Dubois, D. and Prade, H. 1992. Combination of information in the framework of possibility theory. In Data Fusion in Robotics and Machine Intelligence, M. AL Abidi et al.
  24. Barra, V. 2000. Fusion d'Images 3D du Cerveau : Etude de Modèles et Applications. Thèse de doctorat, Université d’Auvergne.
  25. Bloch, I. 1996. Information combination operators for data fusion : a comparative review with classification. IEEE Transactions in systems, Man. and Cybernitics 1, 52-67.
  26. Bezdek, J. C., Keller, J., Krishnapuram, R., and Pal, N. R. 1999. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic, TA 1650.F89.
  27. Cocosco, C. A., Kollokian, V., Kwan, R. K. S., and Evans, A. C. 1997. BrainWeb: Online interface to a 3D MRI simulated brain database. NeuroImage 5, S425.
  28. Lamiche, C., and Moussaoui, A. 2011. Improvement of brain tissue segmentation using information fusion approach. International Journal of Advanced Computer Science and Applications 2, 84–90.
Index Terms

Computer Science
Information Sciences

Keywords

Fusion possibility theory segmentation MR images