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Retinal Vessels Segmentation Based on a Convolutional Neural Network

  • Nadia Brancati
  • Maria Frucci
  • Diego Gragnaniello
  • Daniel Riccio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

We present a supervised method for vessel segmentation in retinal images. The segmentation issue has been addressed as a pixel-level binary classification task, where the image is divided into patches and the classification (vessel or non-vessel) is performed on the central pixel of the patch. The input image is then segmented by classifying all of its pixels. A Convolutional Neural Network (CNN) has been used for the classification task, and the network has been trained on a large number of samples, in order to obtain an adequate generalization ability. Since blood vessels are characterized by a linear structure, we have introduced a further layer into the classic CNN including directional filters. The method has been tested on the DRIVE dataset producing satisfactory results, and its performance has been compared to that of other supervised and unsupervised methods.

Keywords

Retinal image Vessel segmentation Convolutional Neural Network Directional filters 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nadia Brancati
    • 1
  • Maria Frucci
    • 1
  • Diego Gragnaniello
    • 1
  • Daniel Riccio
    • 1
    • 2
  1. 1.Institute for High Performance Computing and Networking National Research Council of Italy (ICAR-CNR)NaplesItaly
  2. 2.University of Naples “Federico II”NaplesItaly

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