一种分割脑磁共振图像的改进FCM聚类算法

doi:10.3969/j.issn.0258-8021.2016.06.002

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Abstract The noise and bias field are main factors lowering the quality of the magnetic resonance imaging. In order to segment brain tissue from MRI image, an anti-noise coherent local intensity fuzzy clustering algorithm (ANCLIFC) was proposed in this wok. By adding a new fuzzy operator and coherent local information as constraints in the cost function, ANCLIFC algorithm exhibited good clustering performance in resisting noise and bias field simultaneously. Twenty synthetic images, 20 simulated brain MRI images from BrainWeb and 100 real brain MRI images from IBSR database were used to evaluate the algorithm′s clustering performance. The experimental results demonstrated that ANCLIFC algorithm had better classification accuracy and stability than other classical modified FCM algorithms for low quality images contaminated by noise and bias field. For synthetic images, the average overall classification accuracy′s SA was 0.97, larger than other algorithms and the best improvement achieved 0.37. For real brain MRI images, ANCLIFC algorithm exhibits obvious superiority in segmenting CSF and the similarity measure′s KI increases about 0.1 in average.

Key words： brain MRI images noise bias field FCM algorithm image segmentation

Received: 30 July 2015

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R318

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	Lin Xiangbo Wang Xinning Guo Dongmei

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Lin Xiangbo Wang Xinning Guo Dongmei. A Modified FCM Clustering Method for Brain Magnetic Resonance Image Segmentation[J]. Chinese Journal of Biomedical Engineering, 2016, 35(6): 648-657.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2016.06.002 OR http://cjbme.csbme.org/EN/Y2016/V35/I6/648

[1] McVeigh ER, Bronskill M, Henkelman R. Phase and sensitivity of receiver coils in magnetic resonance imaging[J]. Medical Physics, 1986, 13(6): 806-814.
[2] Simmons A, Tofts PS, Barker GJ, et al. Sources of intensity nonuniformity in spin echo images at 1.5 T[J].Magnetic Resonance in Medicine, 1994, 32(1): 121-128.
[3] Gudbjartsson H, Patz S.The rician distribution of noisy MRIdata[J]. Magnetic Resonance in Medicine, 1995, 34: 910-914.
[4] Pham DL, Xu Chenyang, Prince JL. A survey of current methods in medical image segmentation[J]. An Annual Review of Biomedical Engineering, 2000, 2(1): 315-337.
[5] Belaroussi B, Milles J, Carme S, et al. Intensity non-uniformity correction in MRI: existing methods and their validation[J]. Medical Image Analysis, 2006, 10(1): 234-246.
[6] Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI[J]. IEEE Transactions on Medical Imaging, 2007, 26(3): 405-421.
[7] Wells WM, Grimson EL, Kikinis R, et al. Adaptive segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 1996, 15(4): 429-442.
[8] Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data[J]. IEEE Transactions on Medical Imaging, 1998, 17(1): 87-97.
[9] The MNI_N3 Software Package. A non-parametric method for correction of intensity non-uniformity in MRI data[[EB/CP]]. http://www.bic.mni.mcgill.ca/software/N3/,2004-03-15/2015-06-10.
[10] Nowak RD. Wavelet-based rician noise removal for magnetic resonance imaging[J]. IEEE Transactions on Image Processing, 1999, 8(10): 1408-1419.
[11] Manjon JV, Carbonell-Caballero J, Lull JJ, et al. MRI denoising using non-local means[J]. Medical Image Analysis, 2008, 12(4): 514-523.
[12] Bezdek JC, Ehrlich R, Full W. The fuzzy c-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2): 191-203.
[13] Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition[J]. Medical Physics, 1992, 20(4): 1033-1048.
[14] 温铁祥,杨丰. 一种结合FCM的Mumford-Shah混合模型MR图像分割方法[J]. 中国生物医学工程学报, 2009, 28(3): 393-397.
[15] 陈志彬, 邱天爽, Su Ruan. 一种基于FCM和Level Set的MRI医学图像分割方法[J]. 电子学报, 2008, 36(9): 1733-1736.
[16] 王顺凤,耿志远,张建伟,等. 改进的非局部脑核磁共振图像分割与偏移场恢复耦合模型[J]. 计算机辅助设计与图形学学报, 2014, 26(9): 1412-1418.
[17] 张承杰,厉力华. 基于空间FCM与MR方法的乳腺MRI序列三维病灶分割研究[J]. 中国生物医学工程学报, 2014, 33(2):202-211.
[18] Pham DL, Prince JL. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities[J]. Pattern Recognition Letters, 1999, 20(1): 57-68.
[19] Pham DL. Spatial models for fuzzy clusteringcomputer vision and image under-standing[J]. Computer Vision and Image Understanding, 2001, 84(2):285-297.
[20] Ahmed MN, Yamany SM, Mohamed N, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199.
[21] Chen Songcan, Zhang Daoqiang. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(4): 1907-1916.
[22] Szilagyi L, Benyo Z, Szilágyi S M. MR brain image segmentation using an enhanced fuzzy c-means algorithm[C]// Proceedings of the 25th Annual International Conference of the Engineering in Medicine and Biology Society. New York: IEEE, 2003: 724-726.
[23] Cai Weiling, Chen Songcan, Zhang Daoqiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007, 40(3): 825-838.
[24] Krinidis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337.
[25] Gong Maoguo, Liang Yan, Shi Jian, et al. Fuzzy C-means clustering with local information and kernelmetric for image segmentation[J]. IEEE Transactions on Image Processing, 2013, 22(2): 573-584.
[26] Li Chunming, Huang Rui, Ding Zhaohua, et al. Avariational level set approach to segmentation and bias correction of images with intensity inhomogeneity[C]//Medical Image Computing and Computer-Assisted Intervention-MIVVAI 2008. Berlin: Springer-Verlag, 2008: 1083-1091.
[27] Li Chunming, Xu Chenyang, Anderson AW, et al. MRI tissue classification and bias field estimation based on coherent local intensity clustering: A unified energy minimization framework[C]//Proceedings of the 21st International Conference on Information Processing in Medical Imaging. Berlin: Springer-Verlag, 2009: 288-299.
[28] Brainweb. Simulated brain database[EB/DB/OL]. http://brainweb.bic.mni.mcgill.ca/brainweb/,2013-12-09/2015-05-19.
[29] IBSR. Internet brain segmentation repository[EB/DB]. http://www.nitrc.org/projects/ibsr,2013-07-05/2015-05-19.