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A new level set method with global alternating minimization algorithm for image segmentation

delete2025-08-21
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PRE
AI
黄
黄魁东 (Kuidong Huang) *
Z
Zhixiang Li
S
Shaojie Tang
F
Fuqiang Yang
W
Wenguang Ye
Y
Yang Zeng
DOI:10.1016/j.apm.2025.116396delete
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Abstract

Abstract

En 中文
• The total variation term with edge information is introduced into convex energy, which improves ability to process weak edges and noise. • An efficient and fast alternating optimization algorithm is designed based on ADMM and FFT. • We eliminate the explicit dependence on the time step and ensure the flexibility of the solution on the time dimension. • The proposed model has a global minimum, showing superior segmentation on industrial CT data.
Keywords:
total variation
convex energy
ADMM
FFT
image segmentation

Journal

Applied Mathematical Modelling cover
Applied Mathematical Modelling
IF:
5.1
Papers:
1.3K
Citations:
2.8W

Organization

M
ministry of industry and information technology
Scholars:
232
Papers: 83
Citations: 0
Cited Papers

Cited Papers

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RESLS: Region and Edge Synergetic Level Set Framework for Image Segmentation
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Many birds, one stone: Medical image segmentation with multiple partially labeled datasets
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errSun, Qing Liu; Zeng, Hailong; Sun, Zhaodong; Li, Xiaobai; Zhao, Guoying; Liang, Yixiong
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A multi-scale level set method based on local features for segmentation of images with intensity inhomogeneity
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PREAI
errMin, Hai; Xia, Li; Han, Junwei; Wang, Xiaofeng; Pan, Qianqian; Fu, Hao; Wang, Hongzhi; Wong, Stephen T. C.; Li, Hai
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UCTNet: Uncertainty-guided CNN-Transformer hybrid networks for medical image segmentation
err2024-08-01
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PREAI
errGuo, Xiayu; Lin, Xian; Yang, Xin; Yu, Li; Cheng, Kwang-Ting; Yan, Zengqiang
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