"Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering f" by Cong Wang, Meng Chu Zhou et al.
 

Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation

Document Type

Article

Publication Date

1-1-2024

Abstract

Since a noisy image has inferior characteristics, the direct use of Fuzzy {C} -Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. To date, only two noise-estimation-based FCM algorithms have been proposed for image segmentation, that is: 1) deviation-sparse FCM (DSFCM) and 2) our earlier proposed residual-driven FCM (RFCM). In this article, we make a thorough comparative study of DSFCM and RFCM. We demonstrate that an RFCM framework can realize more accurate noise estimation than DSFCM when different types of noise are involved. It is mainly thanks to its utilization of noise distribution characteristics instead of noise sparsity used in DSFCM. We show that DSFCM is a particular case of RFCM, thus signifying that they are the same when only impulse noise is involved. With a spatial information constraint, we demonstrate RFCM's superior effectiveness and efficiency over DSFCM in terms of supporting experiments with different levels of single, mixed, and unknown noise.

Identifier

85144063140 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2022.3217897

e-ISSN

21682275

ISSN

21682267

PubMed ID

36455086

First Page

241

Last Page

253

Issue

1

Volume

54

Grant

0012/2019/A1

Fund Ref

National Natural Science Foundation of China

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