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Image segmentation using fuzzy clustering: A survey

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3 Author(s)
Naz, S. ; Dept. of Comput. Sci., Nat. Univ. of Comput. & Emerging Sci., Islamabad, Pakistan ; Majeed, H. ; Irshad, H.

This paper presents a survey of latest image segmentation techniques using fuzzy clustering. Fuzzy C-Means (FCM) Clustering is the most wide spread clustering approach for image segmentation because of its robust characteristics for data classification. In this paper, four image segmentation algorithms using clustering, taken from the literature are reviewed. To address the drawbacks of conventional FCM, all these approaches have modified the objective function of conventional FCM and have incorporated spatial information in the objective function of the standard FCM. The techniques that have been reviewed in this survey are Segmentation for noisy medical images with spatial probability, Novel Fuzzy C-Means Clustering (NFCM), Fuzzy Local Information C-Means (FLICM) Clustering Algorithm and Improved Spatial Fuzzy C-Means Clustering (ISFCM) algorithm.

Published in:

Emerging Technologies (ICET), 2010 6th International Conference on

Date of Conference:

18-19 Oct. 2010