Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images

We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our appr...

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Bibliographic Details
Published inTENCON 2009 - 2009 IEEE Region 10 Conference pp. 1 - 6
Main Authors Alia, O.M., Mandava, R., Ramachandram, D., Aziz, M.E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Biomedical imaging
Clustering algorithms
Digital images
Evolutionary computation
Image analysis
Image segmentation
Magnetic resonance imaging
Radiology
Search methods
Space exploration
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Summary:We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal cluster centers, we use an alternate representation of the search space. Our experiments indicate encouraging results in producing stable clustering for the given problem as compared to using an FCM with randomly initialized cluster centers.
ISBN:9781424445462
1424445469
ISSN:2159-3442
DOI:10.1109/TENCON.2009.5396049