Title
Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network.
Abstract
We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels. Fundus images were normalized before segmentation was performed to enforce consistency in background lighting and contrast. For every effective point in the fundus image, our algorithm extracted three channels of input from the point’s neighbourhood and forwarded the response across the 7-layer network. The output layer consists of four neurons, representing background, optic disc, fovea and blood vessels. In average, our segmentation correctly classified 92.68% of the ground truths (on the testing set from Drive database). The highest accuracy achieved on a single image was 94.54%, the lowest 88.85%. A single convolutional neural network can be used not just to segment blood vessels, but also optic disc and fovea with good accuracy.
Year
DOI
Venue
2017
10.1016/j.jocs.2017.02.006
Journal of Computational Science
Keywords
DocType
Volume
Optic disc segmentation,Blood vessels segmentation,Fovea segmentation,Convolutional neural network,Fundus image
Journal
20
ISSN
Citations 
PageRank 
1877-7503
30
1.07
References 
Authors
37
5
Name
Order
Citations
PageRank
Jen Hong Tan127512.93
Rajendra Acharya U24666296.34
Sulatha V. Bhandary327113.76
Kuang Chua Chua42019.36
Sobha Sivaprasad5723.17