Multiple-Channel Local Binary Fitting Model for Medical Image Segmentation

被引:0
作者
GUO Qi [1 ]
WANG Long [1 ]
SHEN Shuting [2 ]
机构
[1] Department of Mathematics, Harbin Institute of Technology
[2] Health Science Center, Peking University
关键词
Gaussian curve; Penalty term; Multiband active contour model; Image segmentation;
D O I
暂无
中图分类号
TP391.41 [];
学科分类号
080203 ;
摘要
This study proposes an innovative M-L(Multiple-channel local binary fitting) model for medical image segmentation. Designed to improve upon existing image segmentation models, the M-L model introduces a regional limit function to the multi-band active contour model to enable multilayer image segmentation. The Gaussian kernel function is used to improve the previous model’s robustness, necessitating the use of a new initialization curve which enhances the accuracy of segmentation results. Compared to existing image segmentation methods, the proposed M-L model improves numerical stability and efficiency through the introduction of a new penalty term and an increased step length. This simulation experiment verifies the advantages of the new M-L model for improved medical image segmentation, including increased efficiency and usability of the model.
引用
收藏
页码:802 / 806
页数:5
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