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

Reference

[1]J.C. Bezdek (1981), “Pattern Recognition with Fuzzy Objective Function Algorithm”, Plenum, NY.

[2]F.C.H. Rhee, C. Hwang, A Type-2 fuzzy c means clustering algorithm, in: Proc. in Joint 9th IFSA World Congress and 20th NAFIPS International Conference 4, 2001, pp. 1926–1929.

[3]T. Chaira, “A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images”, Applied Soft computing 11(2011) 1711-1717.

[4]Bezdek JC.(1974), “Cluster validity with fuzzy sets”, J Cybern 1974; 3:58–73.

[5]Bezdek JC.(1975), “Mathematical models for systematic and taxonomy”, In: proceedings of eigth international conference on numerical taxonomy, San Francisco; 1975, p. 143–66.

[6]Fukuyama Y, Sugeno M. (1989), “A new method of choosing the number of clusters for the fuzzy c-means method”, In: proceedings of fifth fuzzy system symposium; 1989, p. 247–50.

[7]Xie XL, Beni GA. (1991), “Validity measure for fuzzy clustering”, IEEE Trans Pattern Anal Mach Intell 1991;3:841–6.