Title: Image segmentation of noisy digital images using extended fuzzy C-means clustering algorithm

Authors: Prabhjot Kaur; A.K. Soni; Anjana Gosain

Addresses: Department of Information Technology, Maharaja Surajmal Institute of Technology, C4, Janakpuri, New Delhi, India ' School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India ' University School of Information Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

Abstract: Fuzzy C-Means algorithm fails to segment the noisy image properly. In this paper, we present an algorithm called Extended Fuzzy C means (EFCM), which pre-processes the image to reduce the noise effect and then apply FCM algorithm for image segmentation. Pre-processing of image is influenced by the direct eight neighbourhood pixels of every pixel of an image under consideration. Proposed algorithm has least execution time and it yields regions more homogeneous than those of other techniques. It removes noisy spots and is less sensitive to noise. The proposed technique is a powerful method for noisy image segmentation compared to other image segmentation techniques.

Keywords: fuzzy clustering; robust image segmentation; fuzzy C-means; noisy images; image segmentation; pre-processing.

DOI: 10.1504/IJCAT.2013.054352

International Journal of Computer Applications in Technology, 2013 Vol.47 No.2/3, pp.198 - 205

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 05 Jun 2013 *

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