International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

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

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

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Prabhjot Kaur, Nimmi Chhabra

Index Terms

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


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


[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.