Nonlinear Dynamics, Nonlinear Optics and Lasers, Universitat Politècnica de Catalunya, Terrassa, Spain.
Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Gustavo A. Madero, Ciudad de México, México.
PLoS One. 2019 Jul 25;14(7):e0220132. doi: 10.1371/journal.pone.0220132. eCollection 2019.
Retinal fundus imaging is a non-invasive method that allows visualizing the structure of the blood vessels in the retina whose features may indicate the presence of diseases such as diabetic retinopathy (DR) and glaucoma. Here we present a novel method to analyze and quantify changes in the retinal blood vessel structure in patients diagnosed with glaucoma or with DR. First, we use an automatic unsupervised segmentation algorithm to extract a tree-like graph from the retina blood vessel structure. The nodes of the graph represent branching (bifurcation) points and endpoints, while the links represent vessel segments that connect the nodes. Then, we quantify structural differences between the graphs extracted from the groups of healthy and non-healthy patients. We also use fractal analysis to characterize the extracted graphs. Applying these techniques to three retina fundus image databases we find significant differences between the healthy and non-healthy groups (p-values lower than 0.005 or 0.001 depending on the method and on the database). The results are sensitive to the segmentation method (manual or automatic) and to the resolution of the images.
眼底图像是一种非侵入性的方法，可以观察视网膜血管的结构，其特征可能表明存在糖尿病性视网膜病变（DR）和青光眼等疾病。在这里，我们提出了一种分析和量化诊断为青光眼或 DR 的患者视网膜血管结构变化的新方法。首先，我们使用自动无监督分割算法从视网膜血管结构中提取树状图。该图的节点表示分支（分叉）点和端点，而链接表示连接节点的血管段。然后，我们量化从健康和非健康患者组中提取的图之间的结构差异。我们还使用分形分析来描述提取的图形。将这些技术应用于三个眼底图像数据库，我们发现健康组和非健康组之间存在显著差异（p 值低于 0.005 或 0.001，具体取决于方法和数据库）。结果对分割方法（手动或自动）和图像分辨率敏感。
