An accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corresponding histological sections. The method, firstly, segments the vessel tree and identifies its characteristic points. Then, Global Intensity-Based Features (GIBS) are used to measure the differences in the intensity profiles between arteries and veins. A k-means clustering classifier employs these features to evaluate the potential of artery/vein identification of the proposed method. Finally, a post-processing stage is applied to correct misclassifications using context information and maximize the performance of the classification process. The methodology was validated using an OCT image dataset retrieved from 46 different patients, where 2,392 vessel segments and 97,294 vessel points were manually labeled by an expert clinician. The method achieved satisfactory results, reaching a best accuracy of 93.35% in the identification of arteries and veins, being the first proposal that faces this issue in this image modality.

中文翻译：

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近红外反射视网膜照相中的动脉/静脉血管树识别。

准确识别视网膜动脉和静脉是自动计算机辅助诊断系统开发中的一个相关问题，该系统有助于分析影响血管系统的不同相关疾病，例如糖尿病或高血压等。所提出的方法通过使用光学相干断层扫描（OCT）扫描对视网膜血管树结构进行识别和后分类为动脉和静脉，从而对视网膜血管树结构进行完整的分析。这些扫描包括我们在这项工作中使用的近红外反射视网膜成像图像以及相应的组织学切片。该方法首先对血管树进行分割并识别其特征点。然后，使用基于全局强度的特征（GIBS）来测量动脉和静脉之间强度分布的差异。k 均值聚类分类器利用这些特征来评估所提出的方法的动脉/静脉识别的潜力。最后，应用后处理阶段来使用上下文信息纠正错误分类，并最大限度地提高分类过程的性能。该方法使用从 46 名不同患者检索的 OCT 图像数据集进行了验证，其中由临床专家手动标记了 2,392 个血管段和 97,294 个血管点。该方法取得了令人满意的结果，在动脉和静脉的识别方面达到了93.35%的最佳准确率，是该图像模态中面临此问题的第一个提议。