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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

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A review of feature-based retinal image analysis

Jordan, Kirsty C and Menolotto, Matteo and Bolster, Nigel M and Livingstone, Iain AT and Giardini, Mario E (2017) A review of feature-based retinal image analysis. Expert Review of Ophthalmology. ISSN 1746-9899

[img] Text (Jordan-etal-ERO-2017-A-review-of-feature-based-retinal-image-analysis)
Jordan_etal_ERO_2017_A_review_of_feature_based_retinal_image_analysis.pdf - Accepted Author Manuscript
Restricted to Repository staff only until 22 March 2018.

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Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice.