Investigating post-processing of scanning laser ophthalmoscope images for unsupervised retinal blood vessel detection

Abstract

We explore post-processing of scanning laser ophthalmoscope (SLO) images for the automatic detection of retinal blood vessels. The retinal vasculature is first enhanced using morphological and Gaussian matched filters before a thresholding technique produces a binary vessel map. Such permutations of post-processing techniques are commonly used to achieve unsupervised classification of the vasculature in fundus images, and it is the purpose of this study to investigate their applicability to SLO imaging. We compare the results of vascular detection as performed on SLO and fundus images.