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Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach

Alghamdi, Hanan, Tang, Hongying, Waheeb, SA and Peto, T (2016) Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach In: OMIA3 (MICCAI 2016), 2016-10-17 - 2016-10-21, Athens.

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Abstract

Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance proper- ties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and in exible im- age analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Alghamdi, Hananh.alghamdi@surrey.ac.ukUNSPECIFIED
Tang, HongyingH.Tang@surrey.ac.ukUNSPECIFIED
Waheeb, SAUNSPECIFIEDUNSPECIFIED
Peto, TUNSPECIFIEDUNSPECIFIED
Date : 2016
Copyright Disclaimer : The final publication will be available at Springer http://www.springer.com/gp/computer-science/lncs
Depositing User : Symplectic Elements
Date Deposited : 03 Oct 2016 12:11
Last Modified : 18 Jul 2017 14:26
URI: http://epubs.surrey.ac.uk/id/eprint/812324

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