摘要
针对现有算法因视网膜图像中血管细小和光照等因素导致的分割精度低的问题,在U-Net的基础上进行改进,提出了一种能够较好地提取血管结构的算法模型ASR-UNet。首先,在编码和解码阶段使用了SE-Resnet结构,引入通道注意力机制对血管细微结构进行通道增强,之后在跳跃连接部分使用了AG模块对血管细微结构进行空间增强,提高网络模型对血管细微结构的分割能力。在公开数据集DRIVE和CHASEDB1上验证了本文的算法,在评价指标Acc上分别为0.9697和0.9657,在敏感性上分别为0.8044和0.7673,在特异性指标上为0.9859和0.9866。实验结果表明,近年来的视网膜血管分割算法相比,本文提出的算法在性能有更好的表现。
Factors such as tiny blood vessels in the retina and light will cause the problem of low segmentation accuracy of existing algorithms.This paper proposes a new retinal vessel segmentation algorithm model based on U-Net segmentation model combined with attention mechanism.The improved measures of this paper are as follows:first,we use SE-Resnet to replace the common convolution used in U-Net,thereby introducing a channel attention mechanism to enhance the original features.After that,AG are added to the jump connection part to enhance the spatial characteristics,thereby improving the model?s ability to segment the microstructure of blood vessels.The algorithm of this paper is verified on the public datasets DRIVE and CHASEDB1,and the results are 0.9697 and 0.9657 on Acc,0.8044 and 0.7673 on sensitivity,and 0.9859 and 0.9866 on specificity.Experimental results show that the proposed algorithm has better performance than recent retinal blood vessel segmentation algorithms.
作者
易三莉
陈建亭
贺建峰
YI San-li;CHEN Jian-ting;HE Jian-feng(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,Yunnan,China;Key Laboratory of Computer Technology Application of Yunnan Province,Kunming 650500,Yunnan,China)
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2021年第9期13-20,共8页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(82060329)
云南省教育厅项目(2020J0052)。
作者简介
第一作者:易三莉(1977-),女,博士,讲师,研究方向为数字图像处理、数字信号处理,E-mail:152514845@qq.com;通信作者:贺建峰(1%5-),男,教授,博士生导师,研究方向为医学成像仿真与图像处理分析、医疗信息融合与数据挖掘等,E-mail:120112624@qq.com。
