Automatic Detection of Blood Vessels in Optical Coherence Tomography Scans
Abstract
The aim of this research is to develop a new automated blood vessel (BV) detection algorithm for optical coherence tomography (OCT) scans and corresponding fundus images. The algorithm provides a robust method to detect BV shadows (BVSs) using Radon transformation and other supporting image processing methods. The position of the BVSs is determined in OCT scans and the BV thickness is measured in the fundus images. Additionally, the correlation between BVS thickness and retinal nerve fiber layer (RNFL) thickness is determined. This correlation is of great interest since glaucoma, for example, can be identified by a loss of RNFL thickness.
