Retinal vessel segmentation using Average of Synthetic Exact Filters and Hessian matrix

Abstract

The segmentation of blood vessels in retinal images is an important procedure for the prediction and diagnosis of cardiovascular diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work aims to develop an effective method of retinal vessels segmentation by combining correlation filters and measures extracted from the eigenvalues of the Hessian matrix. The approach uses a threshold to segment the image generated by this combination and is evaluated on two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.