Work

Residual-driven Fuzzy C-Means Clustering for Image Segmentation

Year: 2021

Type: article

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.

Source: IEEE/CAA Journal of Automatica Sinica

DOI: 10.1109/jas.2020.1003420

Authors: Cong Wang, Witold Pedrycz, ZhiWu Li, MengChu Zhou

Institutions: Xidian University, King Abdulaziz University, Macau University of Science and Technology, New Jersey Institute of Technology

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Xidian University, Recruitment Program of Global Experts

Award: G2465600343