Residual-driven Fuzzy C-Means Clustering for Image Segmentation

Document Type

Article

Publication Date

4-1-2021

Abstract

In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and enables noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise. Built on this framework, a weighted ℓ2-norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

Identifier

85096144227 (Scopus)

Publication Title

IEEE Caa Journal of Automatica Sinica

External Full Text Location

https://doi.org/10.1109/JAS.2020.1003420

e-ISSN

23299274

ISSN

23299266

First Page

876

Last Page

889

Issue

4

Volume

8

Grant

0012/2019/A1

Fund Ref

National Natural Science Foundation of China

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