A CNN based algorithm for retinal vessel segmentation

Authors:
Renzo Perfetti;Elisa Ricci;Daniele Casali;Giovanni Costantini
Affiliations:
Department of Electronics and Information Engineering, University of Perugia, Perugia, Italy;Department of Electronics and Information Engineering, University of Perugia, Perugia, Italy;Department of Electronic Engineering, University of Rome "Tor Vergata", Roma, Italy;Department of Electronic Engineering, University of Rome "Tor Vergata", Roma, Italy and Institute of acoustics "O. M. Corbino", Roma, Italy
Venue:
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
Year:
2008

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Abstract

A retinal vessel segmentation method based on cellular neural networks (CNNs) is proposed. The CNN design is characterized by a virtual template expansion obtained through a multi-step operation. It is based on linear, space-invariant 3×3 templates, and can be realized using real-life devices with minor changes. The proposed design is capable to perform vessel segmentation within short computation time. It was tested on a publicly available database of color images of the retina, using receiver operating characteristic (ROC) curves. The simulation results show the good performance, comparable with the best existing methods.