基于多方向滤波的视网膜血管图像分割算法  

Retinal Blood Vessel Segmentation Algorithm Based on Multidirectional Filtering

作　　者：王彩云 关志玉 吴钇达 姚晨 Wang Caiyun;Guan Zhiyu;Wu Yida;Yao Chen(College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing,Jiangsu 211106,China)

机构地区：[1]南京航空航天大学航天学院,江苏南京211106

出　　处：《激光与光电子学进展》2022年第8期453-459,共7页Laser & Optoelectronics Progress

基　　金：国家自然科学基金青年科学基金(61301211);国家留学基金(201906835017);江苏省研究生教育教学改革项目(JGZZ17_008)。

摘　　要：视网膜血管的形态结构具有复杂多变的特点,针对血管图像中的交叉处和延伸处不易分割的问题,提出一种基于多方向滤波的视网膜血管分割算法,该算法进一步提升了血管图像的分割精度。首先,使用直方图均衡化、中值滤波去噪、顶帽变换等方法对绿通道的视网膜血管图像进行增强。然后,对增强后的图像进行多方向的Cake滤波,并对滤波后的结果进行融合,弱化背景中的噪声,增强血管与背景的对比度。最后,采用向量场散度的方法提取阈值并对图像进行分割,得到视网膜血管分割的最终结果。利用公开数据集DRIVE和STARE测试了算法的分割精度,实验结果表明,所提算法能够精确地分割出复杂血管的连接处,灵敏度较高,且执行时间较短。Aiming at the complex and changeable morphological structure of retinal blood vessels,this study proposes a retinal blood vessel segmentation algorithm based on multidirectional filtering to solve the problem that the intersection and extension in blood vessel images are not easy to segment.First,the green channel of the retinal blood vessel image is selected by using histogram equalization,median filter denoising,top hat transformation,and other methods for image enhancement.Then,multidirectional Cake filtering is performed on the enhanced image.The filtered results are fused to weaken the noise in the background and enhance the contrast between the blood vessel and background.Finally,the vector field divergence method is used to extract the threshold,and the image is segmented to obtain the final retinal vessel segmentation result.The algorithms are tested on the public DRIVE and STARE datasets.The experimental results show that the proposed algorithm is simple and effective and adapts to the complex and changeable characteristics of retinal blood vessel scale information.The proposed algorithm can also handle the junction of complex blood vessels and has higher sensitivity,shorter execution time,and higher efficiency than other algorithms.

关 键 词：生物技术 数字图像处理 视网膜血管 图像分割 形态学滤波 向量场散度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]