Improved fuzzy clustering for image segmentation based on a low-rank prior

• 作者: Xiaofeng Zhang Hua Wang Yan Zhang Xin Gao Gang Wang Caiming Zhang
• 来源期刊: 计算可视媒体（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: RESEARCH ARTICLE
• 页码范围: 513-528
• 页数: 16页
• 语种: 英文

摘要

Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis.However,the complexity of medical images makes image segmentation difficult.In recent decades,fuzzy clustering algorithms have been preferred due to their simplicity and efficiency.However,they are sensitive to noise.To solve this problem,many algorithms using non-local information have been proposed,which perform well but are inefficient.This paper proposes an improved fuzzy clustering algorithm utilizing non-local self-similarity and a low-rank prior for image segmentation.Firstly,cluster centers are initialized based on peak detection.Then,a pixel correlation model between corresponding pixels is constructed,and similar pixel sets are retrieved.To improve efficiency and robustness,the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior.Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.