Fractional order distance regularized level set method with bias correction

被引：0
作者：
Cai, Xiumei [1 ]
He, Ningning [1 ]
Wu, Chengmao [2 ]
Liu, Xiao [1 ]
Liu, Hang [1 ]
机构：
[1] School of Automation, Xi’an University of Posts and Telecommunications, Xi’an
[2] School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an
来源：
Journal of China Universities of Posts and Telecommunications | 2024年 / 31卷 / 01期
基金：
中国国家自然科学基金;
关键词：
bias correction; fractional derivative; fractional order distance regularization; image segmentation; level set function;
D O I：
10.19682/j.cnki.1005-8885.2024.2007
中图分类号：
学科分类号：
摘要：
The existing level set segmentation methods have drawbacks such as poor convergence, poor noise resistance, and long iteration times. In this paper, a fractional order distance regularized level set segmentation method with bias correction is proposed. This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function (LSF) and the signed distance function. Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel. Thirdly introducing the offset correction term and fully using the local clustering property of image intensity, the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation. Finally, the fractional distance regularization, offset correction, and external energy constraints are combined, and the energy optimization segmentation method for noisy image is established by level set. Experimental results show that the proposed method can accurately segment the image, and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms. Copyright © 2024, Author. All rights reserved.
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页码：64 / 82
页数：18
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