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Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation

by O. Mema Devi, Shahin Ara Begum
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 9
Year of Publication: 2013
Authors: O. Mema Devi, Shahin Ara Begum
10.5120/12384-8736

O. Mema Devi, Shahin Ara Begum . Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation. International Journal of Computer Applications. 71, 9 ( June 2013), 6-13. DOI=10.5120/12384-8736

@article{ 10.5120/12384-8736,
author = { O. Mema Devi, Shahin Ara Begum },
title = { Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 9 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number9/12384-8736/ },
doi = { 10.5120/12384-8736 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:35:05.423556+05:30
%A O. Mema Devi
%A Shahin Ara Begum
%T Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 9
%P 6-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neuro-fuzzy approach have attracted considerable attention in the computational intelligence and segmentation algorithms have been increasingly in developed in improving the accuracy of medical diagnosis. Fuzzy set attempts to represent the human perception whereas neural network attempt to emulate the architecture and information representation scheme of human brain. In this paper a comparative study on the performance of the FCM and the variant fuzzy competitive learning algorithms including the generalized Kohonen's competitive learning (GKCL)-based algorithms (KCL, fuzzy KCL (FKCL), fuzzy soft KCL (FSKCL)) and the learning vector quantization (LVQ)-based algorithms (LVQ, fuzzy LVQ (FLVQ), fuzzy soft LVQ (FSLVQ)) for MR image segmentation is presented. The performance of the algorithms are evaluated using the standard image quality indices such as MSE (mean squared error) and IQI (image quality index) and the results indicate that the soft versions of fuzzy competitive learning algorithms produces more promising results and require less CPU time than the other learning algorithms. Further, the LVQ-based algorithms have better performance according to the values of MSE and IQI as compared to the KCL based algorithms and the FCM algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

MR image segmentation fuzzy set KCL LVQ

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