An Experimental Assessment of Five Indices of Retinal Vessel Tortuosity with the RET-TORT Public Dataset
We compare the performance of five indices of retinal vessel tortuosity against sampling rates of vessel centerlines. We consider distance measure, tortuosity density, two curvature-based measures, and a recently introduced slope-chain coding for general curves, never before assessed comparatively with retinal vessels. To enable replication of our results, we use the public dataset for retinal tortuosity, RE-TORT. We find that (1) the tortuosity density index offers good performance overall, but is not always the best performer; (2) curvature-based methods are the best if high-frequency resampling is possible, but (3) are the most sensitive to variations of the number of samples; (4) slope-chain coding performs best at low sampling rates. In general, performance may vary considerably with resampling, suggesting that the choice of a tortuosity index for clinical inference requires attention to numerical details, and ideally standardization thereof.