A Cooperative Method to Improve Segmentation of Brain MR Images

Abstract

In this paper, we present a fully unsupervised segmentation process of magnetic resonance image (MRI) of the brain using a data fusion technique and some of ideas of the possibility theory context. The fusion methodology is decomposed into three fundamental phases. We modeling information coming from T2 and PD weighted images in a common framework, in this step an hybridization between FCM and PCM algorithms is retained. In the second phase an operator of fusion is used to combine then this information. Finally, an image of fusion is generated when a decision rule is applied. Some results are presented and discussed using a set of simulated MR image

Keywords

• Fusion,Possibility theory,Segmentation,FPCM,MR images

A cooperative method to improve segmentation of brain MR

Öz

Kaynakça

1. Y. Hata, S. Kobashi, and S. Hirano, “Automated segmentation of human brain mr images aided by fuzzy information granulation and fuzzy inference,” IEEE Trans. SMC, vol. 30, 1998, pp. 381–395.
2. D. Goldberg-Zimring, A. Achiron, and S. Miron, “Automated detection and characterization of multiple sclerosis lesions in brain mr images,” Magnetic Resonance Imaging, vol. 16, 1998, pp.311–318.
3. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, “Automated model-based tissue classification of mr images of the brain,” IEEE Trans. Medical Imaging, vol. 18, 1999, pp.897–908.
4. Y. Wang, T. Adali, J. Xuan, and Z. Szabo, “Magnetic resonance image analysis by information theoretic criteria and stochastic models,” IEEE Trans, Infor. Tech. in Biom., vol. 5, 2001, pp.150–158.
5. I. Bloch, and H. Maitre, “data fusion in 2D and 3D image processing: an overview,” In Proceedings of the X Brazilian symposium on computer graphics and image processing, Brazil, 1997, pp.127–134.
6. C. Bezdek, J. Keller, R. Krishnapuram, and N. R. Pal Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer Academic, TA 1650, F89. 1999.
7. D. Dubois, and H. Prade, Fuzzy Sets and Systems: Theory and Application, New-York: Academic Press, 1980.
8. I. Bloch, “Information combination operators for data fusion: a comparative review with classification”, IEEE Transactions en systems, Man. and Cybernitics, vol. 1, 1996, pp.52–67.

9. J. Z. Hongwei, and O. Basir, “Adaptive Fuzzy Evidential Reasoning for Automated Brain Tissue Segmentation,” In Proceedings of the 7th International Conference of Information Fusion. Stockholm, Sweden, 2004.
10. C. Lamiche, and A. Moussaoui, “Improvement of brain tissue segmentation using information fusion approach,” Int. Jou. of Advan. Comp. Sci. and Applications, vol. 2, 2011, pp.84–90.
11. L. R. Dice, “Measures of the Amount of Ecologic Association Between Species,” Ecology, vol. 26, 1945, pp.297–302.