Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 107

1.

The reading of components of diabetic retinopathy: an evolutionary approach for filtering normal digital fundus imaging in screening and population based studies.

Tang HL, Goh J, Peto T, Ling BW, Al Turk LI, Hu Y, Wang S, Saleh GM.

PLoS One. 2013 Jul 1;8(7):e66730. doi: 10.1371/journal.pone.0066730. Print 2013. Erratum in: PLoS One. 2015;10(3):e0119344.

2.
3.

Automated detection of diabetic retinopathy: results of a screening study.

Bouhaimed M, Gibbins R, Owens D.

Diabetes Technol Ther. 2008 Apr;10(2):142-8. doi: 10.1089/dia.2007.0239.

PMID:
18260777
4.

Rapid grading of fundus photographs for diabetic retinopathy using crowdsourcing.

Brady CJ, Villanti AC, Pearson JL, Kirchner TR, Gupta OP, Shah CP.

J Med Internet Res. 2014 Oct 30;16(10):e233. doi: 10.2196/jmir.3807.

5.

A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image.

Krishnamoorthy S, Alli P.

PLoS One. 2015 May 14;10(5):e0125542. doi: 10.1371/journal.pone.0125542. eCollection 2015. Retraction in: PLoS One. 2017 Jul 18;12 (7):e0181891.

6.

A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy.

Olson JA, Strachan FM, Hipwell JH, Goatman KA, McHardy KC, Forrester JV, Sharp PF.

Diabet Med. 2003 Jul;20(7):528-34.

PMID:
12823232
7.

Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.

Dupas B, Walter T, Erginay A, Ordonez R, Deb-Joardar N, Gain P, Klein JC, Massin P.

Diabetes Metab. 2010 Jun;36(3):213-20. doi: 10.1016/j.diabet.2010.01.002. Epub 2010 Mar 10.

PMID:
20219404
8.

Determination of foveal avascular zone in diabetic retinopathy digital fundus images.

Ahmad Fadzil MH, Izhar LI, Nugroho HA.

Comput Biol Med. 2010 Jul;40(7):657-64. doi: 10.1016/j.compbiomed.2010.05.004. Epub 2010 Jun 22.

PMID:
20573343
9.

Automated diabetic retinopathy imaging in Indian eyes: a pilot study.

Roy R, Lobo A, Pal BP, Oliveira CM, Raman R, Sharma T.

Indian J Ophthalmol. 2014 Dec;62(12):1121-4. doi: 10.4103/0301-4738.149129. Erratum in: Indian J Ophthalmol. 2015 Feb;63(2):176. Lob, Aneesha [corrected to Lobo, Aneesha].

10.

Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy.

Scotland GS, McNamee P, Fleming AD, Goatman KA, Philip S, Prescott GJ, Sharp PF, Williams GJ, Wykes W, Leese GP, Olson JA; Scottish Diabetic Retinopathy Clinical Research Network.

Br J Ophthalmol. 2010 Jun;94(6):712-9. doi: 10.1136/bjo.2008.151126. Epub 2009 Dec 3.

PMID:
19965826
11.
12.

Feasibility study on computer-aided screening for diabetic retinopathy.

Singalavanija A, Supokavej J, Bamroongsuk P, Sinthanayothin C, Phoojaruenchanachai S, Kongbunkiat V.

Jpn J Ophthalmol. 2006 Jul-Aug;50(4):361-6.

PMID:
16897222
13.

Ensemble selection for feature-based classification of diabetic maculopathy images.

Chowriappa P, Dua S, Rajendra Acharya U, Muthu Rama Krishnan M.

Comput Biol Med. 2013 Dec;43(12):2156-62. doi: 10.1016/j.compbiomed.2013.10.003. Epub 2013 Oct 17.

PMID:
24290932
14.

Diabetic retinopathy grading by digital curvelet transform.

Hajeb Mohammad Alipour S, Rabbani H, Akhlaghi MR.

Comput Math Methods Med. 2012;2012:761901. doi: 10.1155/2012/761901. Epub 2012 Sep 12.

15.

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

Scotland GS, McNamee P, Philip S, Fleming AD, Goatman KA, Prescott GJ, Fonseca S, Sharp PF, Olson JA.

Br J Ophthalmol. 2007 Nov;91(11):1518-23. Epub 2007 Jun 21.

16.

The evidence for automated grading in diabetic retinopathy screening.

Fleming AD, Philip S, Goatman KA, Prescott GJ, Sharp PF, Olson JA.

Curr Diabetes Rev. 2011 Jul;7(4):246-52. Review.

PMID:
21644913
17.

Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.

Gardner GG, Keating D, Williamson TH, Elliott AT.

Br J Ophthalmol. 1996 Nov;80(11):940-4.

18.

Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.

Niemeijer M, van Ginneken B, Russell SR, Suttorp-Schulten MS, Abràmoff MD.

Invest Ophthalmol Vis Sci. 2007 May;48(5):2260-7.

19.
20.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

Somasundaram K, Rajendran PA.

ScientificWorldJournal. 2015;2015:534045. doi: 10.1155/2015/534045. Epub 2015 Apr 7.

Supplemental Content

Support Center