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

Computational Visual Media

Yan Zhang¹  Hua Wang¹  Xin Gao¹  Gang Wang¹  Xiaofeng Zhang²  Caiming Zhang³ 

[1] School of Information and Electrical Engineering, Ludong University, 264025, Yantai, China;School of Information and Electrical Engineering, Ludong University, 264025, Yantai, China;Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, 250061, Jinan, China;Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, 250061, Jinan, China;

关键词: image segmentation;    fuzzy clustering;    non-local information;    low-rank prior;    medical images;   

DOI  :  10.1007/s41095-021-0239-3

来源: Springer

【 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 nonlocal 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.

【 授权许可】

CC BY