Residual-driven Fuzzy C-Means Clustering for Image Segmentation  

作　　者：Cong Wang Witold Pedrycz ZhiWu Li MengChu Zhou 

机构地区：[1]Department of Electrical and Computer Engineering,University of Alberta,Edmonton,AB T6R 2V4,Canada [2]School of Electro-Mechanical Engineering,Xidian University,Xi’an 710071,China [3]the Faculty of Engineering,King Abdulaziz University,Jeddah 21589,Saudi Arabia [4]School of Electro-Mechanical Engineering,Xidian University,Xi’an 710071,and also with the Institute of Systems Engineering,Macao University of Science and Technology,Macao,China [5]Institute of Systems Engineering,Macao University of Science and Technology,Macao 999078,China [6]the Helen and John C.Hartmann Department of Electrical and Computer Engineering,New Jersey Institute of Technology,Newark,NJ 07102 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第4期876-889,共14页自动化学报（英文版）

基　　金：supported in part by the Doctoral Students’Short Term Study Abroad Scholarship Fund of Xidian University;the National Natural Science Foundation of China(61873342,61672400,62076189);the Recruitment Program of Global Experts;the Science and Technology Development Fund,MSAR(0012/2019/A1)。

摘　　要：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?2-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.

关 键 词：Fuzzy C-Means image segmentation mixed or unknown noise residual-driven weighted regularization 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]