title: A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering.
abstract: Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).
authors.affiliation: School of Information Science and Technology, Jinan University, Guangzhou 510632, China.
authors.forename: Li
authors.initials: L
authors.lastname: Ma
authors.affiliation: School of Information Science and Technology, Jinan University, Guangzhou 510632, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Information Science and Technology, Jinan University, Guangzhou 510632, China.
authors.forename: Suohai
authors.initials: S
authors.lastname: Fan
authors.affiliation: International School, Jinan University, Guangzhou 510632, China.
authors.forename: Runzhu
authors.initials: R
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D001921:Brain / Q000473:pathology; D016000:Cluster Analysis; D019295:Computational Biology; D005399:Fishes / Q000502:physiology; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods