NDLI: Fuzzy Co-Clustering of medical images using bacterial foraging

Content Provider	IEEE Xplore Digital Library
Author	Hanmandlu, M. Susan, S. Madasu, V.K. Lovell, B.C.
Copyright Year	2008
Description	Author affiliation: Sch. of ITEE, Univ. of Queensland & NICTA, Brisbane, QLD (Lovell, B.C.) \|\| CIPDD/TetraQ, Univ. of Queensland, Brisbane, QLD (Madasu, V.K.) \|\| Delhi Coll. of Eng., Univ. of Delhi, Delhi (Susan, S.) \|\| Dept. of Electr. Eng., I.I.T. Delhi, Delhi (Hanmandlu, M.)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	4390706
Page Count	6
File Format	PDF
ISBN	9781424437801
DOI	10.1109/IVCNZ.2008.4762136
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-26
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Validity Measure Fuzzy Clustering Colour Segmentation Educational institutions Co-Clustering and Bacteria Foraging Blood Image segmentation Histograms Microorganisms Image color analysis Clustering algorithms Lesions Biomedical imaging Biomedical engineering
Content Type	Text
Resource Type	Article

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