title: Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering.
abstract: An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China; Faculty of Computers and Information, Mansoura University, Elgomhouria Street, Mansoura 35516, Egypt.
authors.forename: Ahmed
authors.identifier: 0000-0002-6110-7886
authors.initials: A
authors.lastname: Elazab
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
authors.forename: Changmiao
authors.initials: C
authors.lastname: Wang
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
authors.forename: Fucang
authors.initials: F
authors.lastname: Jia
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
authors.forename: Jianhuang
authors.initials: J
authors.lastname: Wu
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
authors.forename: Guanglin
authors.initials: G
authors.lastname: Li
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
authors.forename: Qingmao
authors.identifier: 0000-0002-2465-0556
authors.initials: Q
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology; D016000:Cluster Analysis; D019295:Computational Biology; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D059906:Neuroimaging / Q000379:methods / Q000706:statistics & numerical data