DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients

Sven Holm; Greg Russell; Vincent Nourrit; Niall McLoughlin

doi:10.1117/1.JMI.4.1.014503

9 February 2017 DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients

Sven Holm, Greg Russell, Vincent Nourrit, Niall McLoughlin

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 1, 014503 (February 2017). https://doi.org/10.1117/1.JMI.4.1.014503

Abstract

A database of retinal fundus images, the DR HAGIS database, is presented. This database consists of 39 high-resolution color fundus images obtained from a diabetic retinopathy screening program in the UK. The NHS screening program uses service providers that employ different fundus and digital cameras. This results in a range of different image sizes and resolutions. Furthermore, patients enrolled in such programs often display other comorbidities in addition to diabetes. Therefore, in an effort to replicate the normal range of images examined by grading experts during screening, the DR HAGIS database consists of images of varying image sizes and resolutions and four comorbidity subgroups: collectively defined as the diabetic retinopathy, hypertension, age-related macular degeneration, and Glaucoma image set (DR HAGIS). For each image, the vasculature has been manually segmented to provide a realistic set of images on which to test automatic vessel extraction algorithms. Modified versions of two previously published vessel extraction algorithms were applied to this database to provide some baseline measurements. A method based purely on the intensity of images pixels resulted in a mean segmentation accuracy of 95.83% (±0.67%), whereas an algorithm based on Gabor filters generated an accuracy of 95.71% (±0.66%).

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Sven Holm, Greg Russell, Vincent Nourrit, and Niall McLoughlin "DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients," Journal of Medical Imaging 4(1), 014503 (9 February 2017). https://doi.org/10.1117/1.JMI.4.1.014503

Received: 24 April 2016; Accepted: 16 January 2017; Published: 9 February 2017

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