Fuzzy Entropy-Based MR Brain Image Segmentation Using Modified Particle Swarm Optimization

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dc.contributor.authorPriya, RK
dc.contributor.authorThangaraj, C
dc.contributor.authorKesavadas, C
dc.contributor.authorKannan, S
dc.date.accessioned2017-03-10T03:26:49Z
dc.date.available2017-03-10T03:26:49Z
dc.date.issued2013
dc.description.abstractThis article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z-function and S-function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy-based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281-288, 2013
dc.identifier.citation23 ,4;281-288en_US
dc.identifier.uri10.1002/ima.22062
dc.identifier.urihttps://dspace.sctimst.ac.in/handle/123456789/9784
dc.publisherINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
dc.subjectEngineering; Optics; Imaging Science & Photographic Technology
dc.titleFuzzy Entropy-Based MR Brain Image Segmentation Using Modified Particle Swarm Optimization
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