文章基本信息

标题：FUZZY CLUSTERING BASED ANT COLONY OPTIMIZATION ALGORITHM FOR MR BRAIN IMAGE SEGMENTATION
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作者：P. HARI KRISHNAN ; DR. P. RAMAMOORTHY
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：65
期号：3
出版社：Journal of Theoretical and Applied
摘要：The conventional methods for segmenting MR brain images with various noises were less effective. In this paper, we aimed for a novel method which intellectually determines the cluster centers before applying the (FCM) fuzzy c-means, thus increasing the iteration efficiency and reducing the computation time. The main feature of this proposed method is to utilize Ant Colony Optimization Algorithm (ACOA) to initialize the cluster centers and classification is made from thereafter using the initial values. Thus it helps to avoid the noisy pixel to be wrongly placed under any of the classes during the iterative process of FCM clustering algorithm, hence a better segmentation of MRI brain images which were scanned for detection of tumors was achieved. The methodology has been successfully carried out on Magnetic Resonance Imaging (MRI) images and efficient segmentation is was carried out on brain tumor images.
关键词：Ant Colony Optimization Algorithm (ACOA); Fuzzy C-Means (FCM); Magnetic Resonance Imaging (MRI); Clustering and Brain Tumor.