›› 2016, Vol. 31 ›› Issue (3): 501-511.doi: 10.1007/s11390-016-1643-5

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2016 • Previous Articles     Next Articles

A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction

Wen-Qian Deng(邓雯倩)1, Xue-Mei Li(李雪梅)1,*, Xifeng Gao(高希峰)2, and Cai-Ming Zhang(张彩明)1   

  1. 1 School of Computer Science and Technology, Shandong University, Jinan 250101, China;
    2 Department of Computer Science, University of Houston, Houston, TX 77004, U.S.A.
  • Received:2015-12-02 Revised:2016-03-15 Online:2016-05-05 Published:2016-05-05
  • Contact: Xue-Mei Li E-mail:xmli@sdu.edu.cn
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61332015, 61373078, 61572292, and 61272430, and the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20110131130004.

In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.

[1] Li C, Gore J C, Davatzikos C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic Resonance Imaging, 2014, 32(7): 913-923.

[2] Condon B R, Patterson J, Wyper D et al. Image nonuniformity in magnetic resonance imaging: Its magnitude and methods for its correction. The British Journal of Radiology, 1987, 60(709): 83-87.

[3] Simmons A, Tofts P S, Barker G J et al. Sources of intensity nonuniformity in spin echo images at 1.5 T. Magnetic Resonance in Medicine, 1994, 32(1): 121-128.

[4] Tincher M, Meyer C R, Gupta R et al. Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI. IEEE Transactions on Medical Imaging, 1993, 12(2): 361-365.

[5] Pham D L, Prince J L. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging, 1999, 18(9): 737-752.

[6] Styner M, Brechbuhler C, Szckely G et al. Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Transactions on Medical Imaging, 2000, 19(3): 153-165.

[7] Van Leemput K, Maes F, Vandermeulen D et al. Automated model-based bias field correction of MR images of the brain. IEEE Transactions on Medical Imaging, 1999, 18(10): 885-896.

[8] Wells III W M, Grimson W E L, Kikinis R et al. Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 1996, 15(4): 429-442.

[9] Johnston B, Atkins M S, Mackiewich B et al. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Transactions on Medical Imaging, 1996, 15(2): 154-169.

[10] Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Transactions on Medical Imaging, 2007, 26(3): 405-421.

[11] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer Science & Business Media, 2013.

[12] Pham D L, Prince J L. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognition Letters, 1999, 20(1): 57-68.

[13] Ahmed M N, Yamany S M, Mohamed N et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199.

[14] Balafar M A, Ramli A R, Mashohor S et al. Compare different spatial based fuzzy-c-mean (FCM) extensions for MRI image segmentation. In Proc. the 2nd International Conference on Computer and Automation Engineering (ICCAE), Feb. 2010, pp.609-611.

[15] Chen S, Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(4): 1907-1916.

[16] Chuang K S, Tzeng H L, Chen S et al. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, 2006, 30(1): 9-15.

[17] Szilagyi L, Benyo Z, Szilagyi S M et al. MR brain image segmentation using an enhanced fuzzy c-means algorithm. In Proc. the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sept. 2003, pp.724-726.

[18] Cai W, Chen S, Zhang D. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007, 40(3): 825-838.

[19] Krinidis S, Chatzis V. A robust fuzzy local information cmeans clustering algorithm. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337.

[20] Gong M, Liang Y, Shi J et al. Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Transactions on Image Processing, 2013, 22(2): 573-584.

[21] Li C, Gatenby C,Wang L et al. A robust parametric method for bias field estimation and segmentation of MR images. In Proc. Computer Vision and Pattern Recognition, June 2009, pp.218-223.

[22] Powell M J D. Approximation Theory and Methods. Cambridge University Press, 1981.

[23] Gong M, Zhou Z, Ma J. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Transactions on Image Processing, 2012, 21(4): 2141-2151.

[24] Ji Z, Liu J, Cao G et al. Robust spatially constrained fuzzy c-means algorithm for brain MR image. Pattern Recognition, 2014, 47(7): 2454-2466.

[25] Bezdek J C. Cluster validity with fuzzy sets. Journal of Cybernetics, 1973, 3(3): 58-73.
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