NDLI: Optimized segmentation for MRI brain images

Content Provider	IEEE Xplore Digital Library
Author	Subashini, P. Jansi, S.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Avinashilingam Inst. of Home Sci. & Higher Educ. for Women Univ., Coimbatore, India (Subashini, P.; Jansi, S.)
Abstract	Magnetic Resonance Imaging plays a pivotal role in pre-surgical evaluation of patients with intractable epilepsy. Research on optimal segmentation process from MRI brain images was carried out. The methods such as K-Means and Fuzzy C Means were done for segmentation of White Matter, Gray Matter and Cerebro-Spinal Fluid tissues. Clustering technique is a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. FCM is more effective to the fuzzy boundary region segment, but the major drawback is that no better way to determine the centroid value of clustering and the initial cluster centers, essentially. FCM is a local search optimization algorithm, it will converge to the local minimum point and this clustering effect would have a greater impact if the initial centroid values are not properly. To overcome this limitation, Genetic Algorithm is integrated with FCM for improving the segmentation performance with higher accuracy rate.
Starting Page	1
Ending Page	6
File Size	2823403
Page Count	6
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7372743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Magnetic resonance imaging Lead Image resolution Epilepsy Optical imaging Optical sensors Genetic Algorithm Magnetic Resonance Imaging Fuzzy C Means K Means Clustering Algorithms
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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