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Automatic Estimation of the Arteriolar-to-Venular Ratio in Retinal Images Using a Graph-Based Approach for Artery/Vein Classification

机译:使用基于图的动脉/静脉分类方法自动估算视网膜图像中的小动脉与静脉比例

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The Arteriolar-to-Venular Ratio (AVR) is a well known index for the diagnosis of diseases such as diabetes, hypertension or cardiovascular pathologies. This paper presents a fully automatic AVR estimation method which uses a graph-based artery/vein classification approach to classify the retinal vessels by a combination of structural information taken from the vasculature graph with intensity features from the original color image. This method was evaluated on the images of the INSPIRE-AVR dataset. The mean error and the correlation coefficient of obtained results with respect to the reference AVR values were identical to the ones obtained by the second observer using a semi-automated system, which demonstrate the potential of the herein proposed solution for clinical application.
机译:动脉-静脉比率(AVR)是用于诊断疾病(例如糖尿病,高血压或心血管疾病)的众所周知的指标。本文提出了一种全自动的AVR估计方法,该方法使用基于图的动脉/静脉分类方法,通过结合来自脉管图的结构信息和原始彩色图像的强度特征,对视网膜血管进行分类。在INSPIRE-AVR数据集的图像上评估了该方法。相对于参考AVR值获得的结果的平均误差和相关系数与使用半自动化系统的第二位观察者获得的结果相同,这证明了本文提出的解决方案在临床上的应用潜力。

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