Retinal Artery-Vein Classification via Topology Estimation


We propose a completely unique, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We tend to build use of the underlying vessel topology to higher classify little and midsized vessels. We have a tendency to extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific options to construct a straightforward, however powerful international likelihood model. We have a tendency to efficiently maximize this model by iteratively exploring the space of doable solutions per the projected vessels. We tested our technique on four retinal datasets and achieved classification accuracies of ninety one.zerop.c, 93.fivep.c, ninety one.sevenpercent, and 90.nine%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the complete vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based methodology could be a probably important tool for diagnosing diseases with retinal vascular manifestation.

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