Quantitative imaging of retinal arteries and veins offers unique insights into cardiovascular and microvascular diseases but is laborious. We developed and tested a method to automatically identify arterial/venular (A/V) vessels in digital retinal images in conjunction with a semi-automatic segmentation technique. Methods of segmentation of blood vessels and the optic disc (OD) was performed as previously described, using a dataset of 10 colour fundus images. Using the OD as a reference a graph representation was constructed using the vessel skeletons. Vessel bifurcations and crossings were identified based on direction and local geometry, and A/V classification was carried out by fuzzy logic classification using colour information. Results were compared with expert classification. Preliminary results showed an average true positive rate for arteries of TPRA=0.83 and TPRV=0.74 for veins. With an overall average of TPRall=0.79 for both vessel type jointly. Computer-based systems can assess local and global aspects of the retinal microvascular architecture, geometry and topology. Automated A/V classification will facilitate efficient cost-effective assessment of clinical images at scale.
M. Elena Martinez-Perez, Kim H. Parker, Nick Witt, Alun D. Hughes, and Simon A. M. Thom "Automatic artery/vein classification in colour retinal images", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331A (31 January 2020);