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A Novel Multiple-kernel based Fuzzy c-means Algorithm with Spatial Information for Medical Image Segmentation
Venu Nookala, B.Anuradha
Pages - 286 - 301     |    Revised - 15-05-2013     |    Published - 30-06-2013
Volume - 7   Issue - 3    |    Publication Date - June 2013  Table of Contents
FCM, Image Segmentation, Gaussian Kernal, Fuzzy, Multiple-Kernal.
Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, a generalized a novel multiple-kernel fuzzy cmeans (FCM) (NMKFCM) methodology with spatial information is introduced as a framework for image-segmentation problem. The algorithm utilizes the spatial neighborhood membership values in the standard kernels are used in the kernel FCM (KFCM) algorithm and modifies the membership weighting of each cluster. The proposed NMKFCM algorithm provides a new flexibility to utilize different pixel information in image-segmentation problem. The proposed algorithm is applied to brain MRI which degraded by Gaussian noise and Salt-Pepper noise. The proposed algorithm performs more robust to noise than other existing image segmentation algorithms from FCM family.
CITED BY (6)  
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4 Venu, N., & Anuradha, B. (2014). Multi-Hyperbolic Tangent Fuzzy C-means Algorithm for MRI Segmentation.
5 Liu Jianwei, Guo & Ray (2014). Brain histogram image segmentation strategy. Xi'an University of Technology, 34 (3), 188-192.
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Mr. Venu Nookala
Alfa college of Engg. - India
Dr. B.Anuradha
Sir Venkateswara University - India