Measurement and classi cation of retinal vascular tortuosity

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthal-mological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classii-cation tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classiication rate of 91% for the rst problem and 95% on the second problem, which connrms that they capture much of the ophthalmologists' notion of tortuosity. Finally, we discuss how the accuracy of these measures can be innuence by the method used to extract the blood vessel segments.