Multiscale Blood Vessel Segmentation in Retinal Fundus Images

Retinal fundus imaging is widely used for eye examinations. The acquired images provide a unique view on the eye vasculature. The analysis of the vasculature has a high importance especially for detecting cardiovascular diseases. We present a multiscale algorithm for automatic retinal blood vessel segmentation, which is considered as a requirement for the diagnosis of vascular diseases. The algorithm uses a Gaussian resolution hierarchy to decrease computational needs, and allows to use of the same methods to detect vessels of different diameters. The algorithm is tested on two public databases using a common notebook. The algorithm segmented each image in less than 20 seconds with a competitive accuracy over 93% in both cases. This proves the applicability for medical applications.