NDLI: Fuzzy C-means with a local membership kl distance for medical image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Gharieb, R. R. Gendy, G.
Copyright Year	2014
Description	Author affiliation: El-Rajhy Liver Hospital, Assiut University, Egypt (Gendy, G.) \|\| Department of Electrical Engineering, Assiut University, 71516, Egypt (Gharieb, R. R.)
Abstract	This paper presents a new technique for incorporating local membership information into the standard fuzzy C-means (FCM) clustering algorithm. In this technique, the objective consists of minimizing the classical FCM function with a unity fuzzifier exponent plus the Kullback-Leibler (KL) information distance acting as a fuzzification and regularization term. The KL distance is proposed to measure the proximity between cluster membership function of a pixel and an average of the cluster membership functions of immediate neighborhood pixels. Therefore, minimizing this KL distance biases the cluster membership of the pixel toward this smoothed membership function of the local neighborhoods. This can provide immunity against noise and results in clustered images with piecewise homogeneous regions. Results of clustering and segmentation of synthetic and real-world medical images are presented to compare the performance of the proposed local membership KL information based FCM (LMKLFCM) and the standard FCM, a local data information based FCM (LDFCM) and a type of local membership information based FCM (LMFCM) algorithms.
Starting Page	47
Ending Page	50
File Size	1586765
Page Count	4
File Format	PDF
ISBN	9781479944132
ISSN	21566100
e-ISBN	9781479944125
DOI	10.1109/CIBEC.2014.7020912
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-11
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation local data and membership information Clustering algorithms KL divergence data clustering Linear programming Robustness fuzzy c-means medical image segmentation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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