The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width measurement under these challenging circumstances and also on two different imaging modalities: color fundus imaging and Scanning Laser Ophthalmoscopy (SLO). In this framework, initially, vessel enhancement is done using linear recursive filtering. Then, a unique combination of morphological operations, background estimation, and iterative thresholding are applied to segment the blood vessels. Further, vessel diameter is estimated in two steps: firstly, vessel centerlines are extracted using the graph-based algorithm. Then, vessel edges are localized from the image profiles, by utilizing spline fitting to obtain vascular orientations and then finding the zero-crossings. Extensive experiments have been carried out on several publicly accessible datasets for vessel segmentation and diameter measurement, i.e., DRIVE, STARE, IOSTAR, RC-SLO and REVIEW dataset. Results demonstrate the competitive and comparable performance than earlier methods. The encouraging quantitative and visual performance of the proposed framework makes it an important component of a decision support system for retinal images.

血管几何形状的变化是与视力和心血管危险因素有关的各种健康问题的指标。这些变化的早期发现和诊断可以帮助患者在疾病处于其主要阶段时选择适当的治疗方案。这些容器的自动分割和量化将降低成本，并消除与手动分级有关的不一致性。但是，在存在视网膜病变和照明不均匀的情况下，自动检测血管是具有挑战性的，这是临床环境中的两种常见情况。本文提出了一个新颖的框架，以解决在这些具有挑战性的情况下以及两种不同的成像方式：彩色眼底成像和扫描激光检眼镜（SLO）下的视网膜血管检测和宽度测量问题。首先，在此框架中，使用线性递归过滤来完成血管增强。然后，将形态学运算，背景估计和迭代阈值的独特组合应用于分割血管。此外，分两步估算血管直径：首先，使用基于图形的算法提取血管中心线。然后，通过利用样条拟合获得血管方向，然后找到零交叉点，从图像轮廓中定位血管边缘。在几个可公开访问的数据集上进行了广泛的实验，用于血管分割和直径测量，即DRIVE，STARE，IOSTAR，RC-SLO和REVIEW数据集。结果表明，与早期方法相比，该方法具有竞争性和可比性。