Automatic artery/vein classification in colour retinal images

M. Elena Martinez-Perez; Kim H. Parker; Nick Witt; Alun D. Hughes; Simon A. M. Thom

doi:10.1117/12.2557519

31 January 2020 Automatic artery/vein classification in colour retinal images

M. Elena Martinez-Perez, Kim H. Parker, Nick Witt, Alun D. Hughes, Simon A. M. Thom

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331A (2020) https://doi.org/10.1117/12.2557519
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

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 TPR_A=0.83 and TPR_V=0.74 for veins. With an overall average of TPR_all=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.

Citation Download Citation

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); https://doi.org/10.1117/12.2557519

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