Prabhjot Kaur; Nimmi Chhabra

International Journal of Intelligent Systems and Applications (IJISA)

IJISA Vol. 4, No. 7, 8 Jun. 2012

Cover page and Table of Contents: PDF (size: 1159KB)

Image Segmentation Techniques for Noisy Digital Images based upon Fuzzy Logic- A Review and Comparison

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Author(s)

Prabhjot Kaur ^1,* Nimmi Chhabra ¹

1. Dept. of Information Technology, Maharaja Surajmal Institute of Technology, GGSIP University, New Delhi, INDIA

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.07.04

Received: 6 Aug. 2011 / Revised: 17 Dec. 2011 / Accepted: 20 Feb. 2012 / Published: 8 Jun. 2012

Index Terms

Fuzzy Clustering, Fuzzy C-Means, Robust Image Segmentation, FCM TYPE-II, Intuitionistic FCM

Abstract

This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (V_pc), Partition entropy (V_pe), Fukuyama-Sugeno (V_fs), and Xie-Beni (V_xb) functions and also compared the performance on the basis of their execution time.

Cite This Paper

Prabhjot Kaur, Nimmi Chhabra, "Image Segmentation Techniques for Noisy Digital Images based upon Fuzzy Logic- A Review and Comparison", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.7, pp.30-36, 2012. DOI:10.5815/ijisa.2012.07.04

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