Automated retinal image quality assessment on the UK Biobank Dataset for Epidemiological Studies

Welikala, R A, Fraz, M M, Foster, P J, Whincup, P H, Rudnicka, A R, Owen, C G, Strachan, D P and Barman, S A (2016) Automated retinal image quality assessment on the UK Biobank Dataset for Epidemiological Studies. Computers in Biology and Medicine, 71, pp. 67-76. ISSN (print) 0010-4825

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Abstract

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.

Item Type: Article
Additional Information: This research has been conducted using the UK Biobank resource. The UK Biobank Eye and Vision Consortium is supported by funding from The Special Trustees of Moorfields Eye Hospital NHS Foundation Trust, and at the NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology. The image analysis work is supported by Grants from the Medical Research Council Population and Systems Medicine Board (Grant no. MR/L02005X/1) and Fight for Sight.
Uncontrolled Keywords: retinal image, image quality, vessel segmentation, large retinal datasets, uk biobank, epidemiological studies
Research Area: Computer science and informatics
Epidemiology and public health
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing > Digital Imaging Research Centre (DIRC)
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Depositing User: Roshan Welikala
Date Deposited: 24 Mar 2016 12:06
Last Modified: 14 Feb 2017 03:30
URI: http://eprints.kingston.ac.uk/id/eprint/34114

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