基于Hesse矩阵和多尺度分析的视网膜动静脉血管管径测量方法  

Measurement of Retinal Diameters of Artery and Vein Based on Hesse Matrix and Multi-scale Analysis

作　　者：肖志涛[1] 崔宁[1] 吴骏[1] 耿磊[1] 张芳[1] 温佳[1] 童军[1] 刘晓婷[1] 杨嵩[1] 

机构地区：[1]天津工业大学电子与信息工程学院,天津300387

出　　处：《电子与信息学报》2016年第11期2871-2878,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61401439);天津市科技支撑计划重点项目(14ZCZDGX00033);天津市应用基础与前沿技术研究计划项目(15JCYBJC16600);高等学校博士学科点专项科研基金(20131201110001)

摘　　要：许多全身性疾病会引起视网膜血管管径及动静脉血管管径比例(Arteriolar-to-Venular diameter Ratios,AVR)的变化,因此对视网膜血管管径进行准确的量化分析对病情诊断具有重要的意义。该文提出一种视网膜动静脉血管管径及AVR的自动测量方法。首先,在分割血管网络的基础上,依据Hesse矩阵检测线状结构的优势,结合多尺度分析准确定位血管方向并计算血管管径;然后利用广义回归神经网络(General Regression Neural Network,GRNN)分类器对动静脉血管骨架线上的点进行准确分类;最后计算感兴趣区域(Region Of Interest,ROI)内的AVR。对REVIEW和DRIVE数据库进行实验,验证了所提方法的有效性。Many systemic diseases can cause changes of the diameters of retinal vessels and Arteriolar-to-Venular diameter Ratios （AVR）, so it is of great importance to make quantitative analysis of the diameter of retinal vessels accurately in the diagnosis of the disease. An automatic method measuring the diameters of the artery and the vein and the AVR is proposed. Firstly, based on the segmentation of vascular network, the diameters of retinal vessels are measured according to the advantage of Hesse matrix for detecting line-like structure, and accurate localization of vascular direction with multi-scale analysis. Secondly, a GenerM Regression Neural Network （GRNN） classifier is used to classify the artery and the vein points. Finally, the AVR in the Region Of Interest （ROI） is calculated. The validity of the proposed method is demonstrated by testing on the DRIVE and the REVIEW database.

关 键 词：视网膜血管 HESSE矩阵 管径测量 广义回归神经网络 视网膜血管管径与动静脉管径比 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]