文章基本信息

标题：MRI BRAIN SEGMENTATION VIA HYBRID FIREFLY SEARCH ALGORITHM
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作者：WALEED ALOMOUSH ; SITI NORUL HUDA SHEIKH ABDULLAH ; SHAHNORBANUN SAHRAN 等
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：61
期号：1
出版社：Journal of Theoretical and Applied
摘要：Magnetic resonance imaging (MRI) brain tumor segmentation is a challenging tasks which include the detection task of tumor from images. In general, this process is done manually by experts in medical images field which is always unclear, because the similarity between normal and abnormal tissues. The present study proposes a new clustering approach based on the hybridization of firefly algorithm (FA) and fuzzy c-means algorithm(FCM) called (FFCM) to segment MRI brain images. this approach use the capability of firefly search to find optimal initial cluster centres for the FCM and thus improve (MRI) brain tumor segmentation. The proposed approach was evaluated by applying it to a magnetic resonance imaging (MRI) brain segmentation problem using a simulated brain data set of McGill University and real MRI images from Internet Brain Segmentation Repository benchmark data sets. The cluster validity index (Rm) was used as a fitness function to determine the best solutions obtained by the firefly algorithm. The experiments indicated encouraging results after applying FFCM, compared with the outcomes of state-of-the-art segmentation algorithms and FCM random initialization of cluster centres.
关键词：Fuzzy c-means; Firefly algorithm; MRI Images segmentation; Rm validity index