Fractional order distance regularized level set method with bias correction

doi:10.19682/j.cnki.1005-8885.2024.2007

The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (1): 64-82.doi: 10.19682/j.cnki.1005-8885.2024.2007

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Fractional order distance regularized level set method with bias correction

Cai Xiumei, He Ningning, Wu Chengmao, Liu Xiao, Liu Hang

1. School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 2. School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Received:2022-08-17 Revised:2024-01-06 Accepted:2024-02-22 Online:2024-02-29 Published:2024-02-29
Contact: Corresponding author: He Ningning, E-mail: heningning@stu.xupt.edu.cn E-mail:heningning@stu.xupt.edu.cn
Supported by:
The author would like to thank the co-author for the valuable suggestions and comments. This work was supported by the National Natural Science Foundation of China (62071378).

Abstract

Abstract: 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.

Key words: image segmentation, fractional order distance regularization, level set function, fractional derivative, bias correction

Cai Xiumei, He Ningning, Wu Chengmao, Liu Xiao, Liu Hang. Fractional order distance regularized level set method with bias correction[J]. The Journal of China Universities of Posts and Telecommunications, 2024, 31(1): 64-82.

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Fractional order distance regularized level set method with bias correction

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