吉林大学学报（信息科学版）2024，Vol.42Issue(4)：690-699,10.
基于多方向特征和连通性检测的眼底图像分割
Vessel Image Segmentation Based on Multi-Directional Features and Connectivity Detection
摘要
Abstract
Fundus images often contain a large number of small blood vessels with significant noise interference and blurred boundaries,making segmentation challenging.To address these characteristics,a fundus image segmentation method called MDF_Net&CD(Multi-Directional Features neural Network and Connectivity Detection)is proposed,based on multidirectional features and connectivity detection.A deep neural network model,MDF_Net(Multi-Directional Features neural Network),is designed to take different directional feature vectors of pixels as input.MDF_Net is used for the initial segmentation of the fundus images.A connectivity detection algorithm is proposed to revise the preliminary segmentation results of MDF_Net,according to the geometric characteristics of blood vessels.In the public fundus image dataset,MDF_Net&CD is compared with recent representative segmentation methods.The experimental results show that MDF_Net&CD can effectively capture the detailed characteristics of pixels,and has a good segmentation effect on irregular,severely noisy,and blurred boundaries of small blood vessels.The evaluation indices are balanced,and the sensitivity,F1 score,and accuracy are better than other methods participating in the comparison.关键词
眼底血管分割/多方向特征/连通性检测/深度神经网络Key words
vessel image segmentation/multi-directional features/connectivity detection/deep neural network分类
信息技术与安全科学引用本文复制引用
窦全胜,李丙春,刘静,张家源..基于多方向特征和连通性检测的眼底图像分割[J].吉林大学学报（信息科学版）,2024,42(4):690-699,10.基金项目
国家自然科学基金资助项目(61976124 （）
61976125) （）
新疆维吾尔自治区自然科学基金资助项目(2022D01A237 （）
2022D01A238) （）
