  Systemic diseases such as diabetes, hypertension, atherosclerosis are among
the leading causes of annual human mortality rate. It is suggested that retinal
and conjunctival vascular tortuosity is a potential biomarker for such systemic
diseases. Most importantly, it is observed that the tortuosity depends on the
thickness of these vessels. Therefore, selective calculation of tortuosity
within specific vessel thicknesses is required depending on the disease being
analysed. In this paper, we propose a thickness sensitive vessel extraction
framework that is primarily applicable for studies related to retinal and
conjunctival vascular tortuosity. The framework uses a Convolutional Neural
Network based on the IterNet architecture to obtain probability maps of the
entire vasculature. They are then processed by a multi-scale vessel enhancement
technique that exploits both fine and coarse structural vascular details of
these probability maps in order to extract vessels of specified thicknesses. We
evaluated the proposed framework on four datasets including DRIVE and SBVPI,
and obtained Matthew's Correlation Coefficient values greater than 0.71 for all
the datasets. In addition, the proposed framework was utilized to determine the
association of diabetes with retinal and conjunctival vascular tortuosity. We
observed that retinal vascular tortuosity (Eccentricity based Tortuosity Index)
of the diabetic group was significantly higher (p < .05) than that of the
non-diabetic group and that conjunctival vascular tortuosity (Total Curvature
normalized by Arc Length) of diabetic group was significantly lower (p < .05)
than that of the non-diabetic group. These observations were in agreement with
the literature, strengthening the suitability of the proposed framework.
