DocumentCode :
2478511
Title :
Modified fuzzy multi-thresholding algorithm for segmentation of MRI
Author :
Yang, Yong
Author_Institution :
Sch. of Inf. Manage., Jiangxi Univ. of Finance & Econ., Nanchang
fYear :
2008
fDate :
25-27 June 2008
Firstpage :
1131
Lastpage :
1136
Abstract :
To overcome the drawbacks of fuzzy multi-level entropic thresholding algorithm, a modified fuzzy multi-level thresholding algorithm for segmentation of MRI is presented in this paper. The algorithm is different from the entropic thresholding algorithm including two aspects. First, maximum variance criteria is used in the proposed algorithm to overcome the limitation of entropic algorithm, which usually requires the resemble distribution of object and background. Second, genetic algorithm is employed to search for the optimal parameters in ours instead of simulated annealing in entropic thresholding algorithm. Besides, the minimum cross-entropy algorithm is applied for masking before segmentation by taking into account the characteristic of MRI. Experimental results show that the proposed method can accurately and efficiently segment the MRI and outperforms the existing ones using the fuzzy entropic thresholding principle.
Keywords :
biomedical MRI; fuzzy logic; genetic algorithms; image segmentation; simulated annealing; fuzzy multi-level entropic thresholding; genetic algorithm; image segmentation; magnetic resonance imaging; maximum variance criteria; modified fuzzy multi-thresholding algorithm; simulated annealing; Automation; Finance; Fuzzy control; Genetic algorithms; Information management; Intelligent control; Magnetic resonance imaging; Simulated annealing; MRI; fuzzy thresholding; genetic algorithm; maximum variance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
Type :
conf
DOI :
10.1109/WCICA.2008.4593082
Filename :
4593082
Link To Document :
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