  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.
