Fuzzy Co-Clustering of Medical Images using Bacterial Foraging

Hanmandlu, M., Susan, S., Madasu, V.K. and Lovell, B.C. (2008). Fuzzy Co-Clustering of Medical Images using Bacterial Foraging. In: Irie, K. and Pairman, D., 2008 23rd International Conference Image and Vision Computing New Zealand. Image and Vision Computing New Zealand 2008, Lincoln University, New Zealand, (1-6). 26-28 November, 2008. doi:10.1109/IVCNZ.2008.4762136


Author Hanmandlu, M.
Susan, S.
Madasu, V.K.
Lovell, B.C.
Title of paper Fuzzy Co-Clustering of Medical Images using Bacterial Foraging
Conference name Image and Vision Computing New Zealand 2008
Conference location Lincoln University, New Zealand
Conference dates 26-28 November, 2008
Convener Irie, K.
Proceedings title 2008 23rd International Conference Image and Vision Computing New Zealand
Journal name 2008 23rd International Conference Image and Vision Computing New Zealand, IVCNZ
Place of Publication USA
Publisher IEEE
Publication Year 2008
Sub-type Fully published paper
DOI 10.1109/IVCNZ.2008.4762136
Open Access Status
ISBN 978-1-4244-2582-2
Editor Irie, K.
Pairman, D.
Volume 1
Start page 1
End page 6
Total pages 6
Language eng
Abstract/Summary A novel modification of the Fuzzy Clustering for Categorical Multivariate date (FCCM) algorithm termed as dasiaFuzzy Co-Clustering Algorithm for Images (FCCI)psila is proposed for clustering of medical images. The main aim of this work is to segment regions of interest in histo-pathological images which consist of groups of similar cells indicating some form of abnormality in the animal tissue. The proposed method relies on improved colour clustering results when FCCI is applied on images as compared to the conventional clustering techniques. The method also categorizes different types of lesions based on the co-clustering results. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. The colour segmentation results are found to be more accurate, producing well formed and valid clusters having ldquocrisppsila values of membership function with lesser number of iterations. The algorithm results in distinct co-clusters ranked in the order of their memberships.
Subjects E1
080106 Image Processing
970109 Expanding Knowledge in Engineering
Keyword Fuzzy Clustering
Validity Measure
Colour Segmentation
Co-Clustering
Bacteria Foraging
Q-Index Code E1
Q-Index Status Confirmed Code

 
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Created: Fri, 17 Apr 2009, 02:12:22 EST by Barb Clyde on behalf of Centre for Integrated Preclinical Drug Development