Age-related macular degeneration detection and stage classification using choroidal OCT images

Deng, Jingjing, Xie, Xianghua, Terry, Louise ORCID: https://orcid.org/0000-0002-6200-8230, Wood, Ashley ORCID: https://orcid.org/0000-0002-9312-6184, White, Nick, Margrain, Tom H. ORCID: https://orcid.org/0000-0003-1280-0809 and North, Rachel V. ORCID: https://orcid.org/0000-0002-6657-5099 2016. Age-related macular degeneration detection and stage classification using choroidal OCT images. Presented at: ICIAR 2016, Póvoa de Varzim, Portugal, 13-15 Jul 2016. Published in: Campilho, Aurélio and Karray, Fakhri eds. Image Analysis and Recognition: ICIAR 2016. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.9730 Cham, Switzerland: Springer Verlag, pp. 707-715. 10.1007/978-3-319-41501-7_79

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Abstract

Age-Related Macular Degeneration (AMD) is a progressive eye disease which damages the retina and causes visual impairment. Detecting those in the early stages at most risk of progression will allow more timely treatment and preserve sight. In this paper, we propose a machine learning based method to detect AMD and distinguish the different stages using choroidal images obtained from optical coherence tomography (OCT). We extract texture features using a Gabor filter bank and non-linear energy transformation. Then the histogram based feature descriptors are used to train the random forests, Support Vector Machine (SVM) and neural networks, which are tested on our choroid OCT image dataset with 21 participants. The experimental results show the feasibility of our method.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Optometry and Vision Sciences
Publisher:	Springer Verlag
ISBN:	978-3-319-41501-7
ISSN:	0302-9743
Date of First Compliant Deposit:	23 January 2019
Last Modified:	12 Nov 2023 09:38
URI:	https://orca.cardiff.ac.uk/id/eprint/118432

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