Literature DB >> 16505050

Automated assessment of diabetic retinal image quality based on clarity and field definition.

Alan D Fleming1, Sam Philip, Keith A Goatman, John A Olson, Peter F Sharp.   

Abstract

PURPOSE: To evaluate the performance of an automated retinal image quality assessment system for use in automated diabetic retinopathy grading.
METHODS: Algorithmic methods have been developed for assessing the quality of 45 degrees single field retinal images for use in diabetic retinopathy screening. For this purpose, image quality was defined by two aspects: image clarity and field definition. An image with adequate clarity was defined as one that shows sufficient detail for automated retinopathy grading. The visibility of the macular vessels was used as an indicator of image clarity, since these vessels are known to be narrow and become less visible with any image degradation. An image with adequate field definition was defined as one that shows the desired field of view for retinopathy grading, including the full 45 degrees field of view, the optic disc, and at least two optic disc diameters of visible retina around the fovea. From 489 patients attending a diabetic retinopathy screening program, 1039 retinal images were obtained. The images were graded by a clinician for image clarity and field definition, with a comprehensive image-quality grading scheme.
RESULTS: The sensitivity and specificity were, respectively, 100% and 90.9% for inadequate clarity detection, 95.3% and 96.4% for inadequate field definition detection, and 99.1% and 89.4% for inadequate overall quality detection.
CONCLUSIONS: The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.

Entities:  

Mesh:

Year:  2006        PMID: 16505050     DOI: 10.1167/iovs.05-1155

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  27 in total

Review 1.  Automated quality assessment of retinal fundus photos.

Authors:  Jan Paulus; Jörg Meier; Rüdiger Bock; Joachim Hornegger; Georg Michelson
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-05-19       Impact factor: 2.924

2.  The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme.

Authors:  S Philip; A D Fleming; K A Goatman; S Fonseca; P McNamee; G S Scotland; G J Prescott; P F Sharp; J A Olson
Journal:  Br J Ophthalmol       Date:  2007-05-15       Impact factor: 4.638

3.  Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine.

Authors:  Sajib Kumar Saha; Basura Fernando; Jorge Cuadros; Di Xiao; Yogesan Kanagasingam
Journal:  J Digit Imaging       Date:  2018-12       Impact factor: 4.056

4.  Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images.

Authors:  Carla Agurto; E Simon Barriga; Victor Murray; Sheila Nemeth; Robert Crammer; Wendall Bauman; Gilberto Zamora; Marios S Pattichis; Peter Soliz
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-07-29       Impact factor: 4.799

5.  Quality evaluation of digital fundus images through combined measures.

Authors:  Diana Veiga; Carla Pereira; Manuel Ferreira; Luís Gonçalves; João Monteiro
Journal:  J Med Imaging (Bellingham)       Date:  2014-04-23

6.  Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs.

Authors:  Kevin Tozer; Maria A Woodward; Paula A Newman-Casey
Journal:  J Endocrinol Diabetes       Date:  2015-11-11

7.  DrishtiCare: a telescreening platform for diabetic retinopathy powered with fundus image analysis.

Authors:  Gopal Datt Joshi; Jayanthi Sivaswamy
Journal:  J Diabetes Sci Technol       Date:  2011-01-01

8.  Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland.

Authors:  G S Scotland; P McNamee; S Philip; A D Fleming; K A Goatman; G J Prescott; S Fonseca; P F Sharp; J A Olson
Journal:  Br J Ophthalmol       Date:  2007-06-21       Impact factor: 4.638

Review 9.  Application of artificial intelligence in ophthalmology.

Authors:  Xue-Li Du; Wen-Bo Li; Bo-Jie Hu
Journal:  Int J Ophthalmol       Date:  2018-09-18       Impact factor: 1.779

10.  Automated Method of Grading Vitreous Haze in Patients With Uveitis for Clinical Trials.

Authors:  Christopher L Passaglia; Tia Arvaneh; Erin Greenberg; David Richards; Brian Madow
Journal:  Transl Vis Sci Technol       Date:  2018-03-23       Impact factor: 3.283

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