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

Xiaofeng Zhang; Hua Wang; Yan Zhang; Xin Gao; Gang Wang; Caiming Zhang

doi:10.1007/s41095-021-0239-3

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Research Article | Open Access

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

Xiaofeng Zhang^{¹^,²}, Hua Wang^¹(

), Yan Zhang^¹, Xin Gao^¹, Gang Wang^¹, Caiming Zhang^²

1School of Information and Electrical Engineering, LudongUniversity, Yantai 264025, China

2Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, Jinan 250061, China

Show Author Information

Abstract

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.

Keywords

image segmentation fuzzy clustering non-local information low-rank prior medical images

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Computational Visual Media

Volume 7 Issue 4,
December 2021

Pages 513-528

DOI: 10.1007/s41095-021-0239-3

Cite this article:

Zhang X, Wang H, Zhang Y, et al. Improved fuzzy clustering for image segmentation based on a low-rank prior. Computational Visual Media, 2021, 7(4): 513-528. https://doi.org/10.1007/s41095-021-0239-3

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Received: 26 February 2021

Accepted: 15 May 2021

Published: 05 August 2021

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