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Topological data analysis of high resolution diabetic retinopathy images

Lookup NU author(s): Kathryn Garside, Professor Robin Henderson, Dr Irina Makarenko

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2019 Garside et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Diabetic retinopathy is a complication of diabetes that produces changes in the blood vessel structure in the retina, which can cause severe vision problems and even blindness. In this paper, we demonstrate that by identifying topological features in very high resolution retinal images, we can construct a classifier that discriminates between healthy patients and those with diabetic retinopathy using summary statistics of these features. Topological data analysis identifies the features as connected components and holes in the images and describes the extent to which they persist across the image. These features are encoded in persistence diagrams, summaries of which can be used to discrimate between diabetic and healthy patients. The method has the potential to be an effective automated screening tool, with high sensitivity and specificity.

Publication metadata

Author(s): Garside K, Henderson R, Makarenko I, Masoller C

Publication type: Article

Publication status: Published

Journal: PLoS ONE

Year: 2019

Volume: 14

Issue: 5

Online publication date: 24/05/2019

Acceptance date: 10/05/2019

Date deposited: 13/06/2019

ISSN (electronic): 1932-6203

Publisher: Public Library of Science

URL: https://doi.org/10.1371/journal.pone.0217413

DOI: 10.1371/journal.pone.0217413

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