ABSTRACT: Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity in-homogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. Here, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation with bias field correction. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.
[1] U. Vovk, F. Pernus, and B. Likar, "A review of methods for correction of intensity inhomogeneity in MRI," IEEE Trans.Med. Imag., vol. 26, no. 3, pp. 405–421, Mar. 2007.
[2] W. Wells, E. Grimson, R. Kikinis, and F. Jolesz, "Adaptive segmentation of MRI data," IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 429–442, Apr. 1996.
[3] V. Leemput, K. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based bias field correction of MR images of the brain," IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 885–896, Oct. 1999.
[4] Y. Zhang, M. Brady, and S. Smith, "Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm," IEEE Trans.Med. Imag., vol. 20, no. 1, pp. 45– 57, Jan. 2001.
[5] C. Li, C. Gatenby, L. Wang, and J. Gore, "A robust parametric method for bias field estimation and segmentation of MR images," in Proc. IEEE Conf. Comput. Vision Pattern Recog., 2009, pp. 218–223.
[6] C. Li, C. Xu, A. Anderson, and J. Gore, "MRI tissue classification and bias field estimation based on coherent local intensity clustering: A unified energy minimization framework," in Proc. 21st Int. Conf. Inf. Process. Med. Imag., Lecture Notes in Computer Science, 2009, vol. 5636, pp. 288–299.
[7] K. Sikka, N. Sinha, P. K. Singh, and A. K. Mishra, "A fully automated algorithm under modified FCM framework for improved brain MR image segmentation," Magn. Reson. Imag., vol. 27, pp. 994–1004, Jul.2009.
[8] D. Pham and J. Prince, "Adaptive fuzzy segmentation of magnetic resonance images," IEEE Trans.Med. Imag., vol. 18, no. 9, pp. 737–752, Sep.1999.
[9] M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T.Moriarty, "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data," IEEE Trans. Med. Imag., vol. 21, no. 3, pp. 193–199, Mar. 2002.