Vessel structure is one of the most important parts of the retina which physicians can detect many diseases by analysing its features. Localization of blood vessels in retina images is an important process in medical image analysis. This process is also more challenging with the presence of bright and dark lesions. In this thesis, two automated vessel localization methods to handle both healthy and unhealthy (pathological) retina images are analyzed. Each method consists of two major steps and the second step is the same in the two methods. In the first step, an algorithm is used to decrease the effect of bright lesions. In Method 1, this algorithm is based on K- Means segmentation, and in Method 2, it is based on a regularization procedure. In the second step of both methods, a multi-scale line operator is used to localize the line-shaped vascular structures and ignore the dark lesions which are generally assumed to have irregular patterns. After the introduction of the methods, a detailed quantitative and qualitative comparison of the methods with one another as well as the state-of-the-art solutions in the literature based on the segmentation results on the images of the two publicly available datasets, DRIVE and STARE, is reported. The results demonstrate that the methods are highly comparable with other solutions.

本文研究了两种自动化的血管定位方法，用于处理健康和不健康（病理性）视网膜图像，通过降低亮斑效应和使用多尺度线运算符定位线状血管结构来忽略黑斑病变。通过与现有解决方案的定量和定性比较，结果表明这些方法与其他解决方案高度可比。

视网膜图像中血管的自动定位