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, RETTORT. 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, but the length of the linear elements must be chosen carefully. 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.