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Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm
Joint Authors
Yang, Zhang
Shufan, Ye
Weifeng, Ding
Li, Guo
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-15
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems.
The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect.
Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm.
First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm.
Then, the optimal value was obtained using the improved HS algorithm.
The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images.
Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm.
In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.
American Psychological Association (APA)
Yang, Zhang& Shufan, Ye& Li, Guo& Weifeng, Ding. 2016. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm. BioMed Research International،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1097799
Modern Language Association (MLA)
Yang, Zhang…[et al.]. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm. BioMed Research International No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1097799
American Medical Association (AMA)
Yang, Zhang& Shufan, Ye& Li, Guo& Weifeng, Ding. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1097799
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1097799