This page contains a Flash digital edition of a book.
Computer-aided Analysis of Fundus Photographs


Automated Computer-aided Analysis of Fundus Digital Photographs in Diabetic Retinopathy Screening


The development of systematic programmes of screening for retinopathy has been identified as an urgent healthcare need. Studies have indicated that the severity of vision loss due to diabetes is largely due to lack of screening.18


We have developed and evaluated a novel two-step approach that automatically screens colour fundus photographs in patients with the use of sequential examinations from the same patient to analyse the evolution of the disease in that patient.19


The automated


grading system, RetmarkerSR consists of software earmarking microaneurysms and ‘red-dot-like’ vascular lesions. The system includes a co-registration algorithm that allows comparisons between different screenings for the same eye in the same retinal location. In a first-step single analysis, the system generates one of two possible outputs – ‘disease’ or ‘no disease’. The ‘disease’ category includes images where vascular lesions are found in the central macula that correspond to level 35 and above of the ETDRS scale, including


1. Bernardes R, Nunes S, Pereira I, et al., Computer-assisted microaneurysm turnover in the early stages of diabetic retinopathy, Ophthalmologica, 2009;223:284–91.


2. Nunes S, Pires I, Rosa A, et al., Microaneurysm turnover is a biomarker for diabetic retinopathy progression to clinically significant macular edema: findings for type 2 diabetics with nonproleferative retinopathy, Ophthalmologica, 2009;223:292–7.


3. Hejlesen O, Ege B, Englmeier K-H, et al., TOSCA imaging – developing Internet based image processing software for screening and diagnosis of diabetic retinopathy, Stud Health Technol Inform, 2004;107(Pt 1):222–6.


4. Parsons-Wingerter P, Radhakrishman K, Vickerman M, Kaiser P, Oscillation of angiogenesis with vascular dropout in diabetic retinopathy by VESsel GENeration analysis (VESGEN), Inv Ophthalm, 2010;51(1):498–507.


5. Englmeier K-H, Schmid K, Hildebrand C, et al., Early detection of diabetes retinopathy by new algorithms for automatic recognition of vascular changes, Eur J Med Res, 2004;9:473–8.


6. Klein R, Klein B, Moss SE, Cruickshanks KJ, The Wisconsin Epidemiologic Study of Diabetic Retinopathy. XV. The long-term incidence of macular edema, Ophthalmology, 1995;102:7–16.


7. Csaky KG, Richman EA, Ferris FL, Report from the NEI/FDA Ophthalmic Clinical Trial Design and Endpoints Symposium, Invest Ophthalmol Vis Sci, 2008;49:479–89.


maculopathy, advanced non-proliferative retinopathy and proliferative retinopathy. In the one-step analysis, the algorithm detects the presence of red-dot-like lesions in fields 1 and 2 (field 1 is centred on the optic disc and field 2 is centred on the fovea). The algorithm combines this initial analysis with a second analysis that compares two different and consecutive examinations for the same patient from two successive screenings with an interval of approximately one year. The images from the field centred on the macula are co-registered to complete a difference analysis that will indicate disease activity in the central 3,000 µm diameter circle of the macula. The results show a clear improvement over currently available fully automated screening algorithms, with a sensitivity of 95.8 % and a specificity of 63.2 %. RetmarkerSR was shown to identify urgent cases for referral and will therefore allow the burden of manual grading to be reduced. This two-step analysis shows a clear improvement in specificity over other available automated systems.20,21


The integration of this technique into


a yearly screening programme is expected to result in a progressive decrease in the burden of manual grading by safely decreasing the number of false-positive results to be manually graded, with economic advantages, making DR screening more feasible. n


8. Kohner AM, Stratton IM, Aldington SJ, et al. for the UK Prospective Study Group, Microaneurysms in the development of diabetic retinopathy (UKPDS 42), Diabetologia, 1999;45:1107–12.


9. Klein R, Meuer SM, Scot E, et al., The relationship of retinal microaneurysm counts to the 4-year progression of diabetic retinopathy, Arch Ophthalmol, 1989;107:1780–5.


10. Sjølie AK, Klein R, Porta M, et al., Retinal microaneurysm count predicts progression and regression of diabetic retinopathy. Post-hoc results from the DIRECT Programme, Diabet Med, 2011;28(3):345–51.


11. Torrent-Solans T, Duarte L, Monteiro R, et al., Red dots counting on digitalized fundus images of mild nonproliferative retinopathy in Diabetes type 2, ARVO Meeting Abstracts, 2004;45:2985.


12. Sharp PF, Olson J, Strachan F, Hipwell J, Ludbrook A, O’Donnell M, Wallace S, Goatman K, Grant A, Waugh N, McHardy K, Forrester JV, The value of digital imaging in diabetic retinopathy, Health Technol Assess, 2003;7:1–119.


13. Lobo CL, Bernardes RC, Figueira JP, et al., Three-year follow-up of blood retinal barrier and retinal thickness alterations in patients with type 2 diabetes mellitus and mild nonproliferative retinopathy, Arch Ophthalmol, 2004;122:211–7. 14. Ulbig M, et al., Personal communication, 2011.


15. Cunha-Vaz J, An integrated perspective on diabetic retinopathy in type 2 diabetes: 10.4 Characterization of retinopathy phenotypes. In: Cunha-Vaz J (ed), Diabetic Retinopathy, Singapore: World Scientific Publishing, 2010;296–300.


16. Bernardes R, Santos T, Serranho P, et al., Noninvasive evaluation of retinal leakage using OCT, Ophthalmologica, 2011;226:29–36.


17. Byeon SH, Hwanf Chu Y, Lee H, et al., Foveal ganglion cell layer damage in ischemic diabetic maculopathy. Correlation of optical coherence tomographic and anatomic changes, Ophthalmology, 2009;116(10):1949–59.


18. Javitt JC, Aiello LP, Chiang Y, et al., Preventive eye care in people with diabetes is cost-saving to the federal government. Implications for health-care reform, Diabetes Care, 1994;17(8):909–17.


19. Oliveira C, Cristovão L, Ribeiro M, Faria Abreu J, Improved automated screening of diabetic retinopathy, Ophthalmologica, 2011; [Epub ahead of print].


20. Usher D, Dumsky M, Himaga M, et al., Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening, Diabet Med, 2003;21:84–90.


21. Abràmoff RJ, Russell SR, Folk JC, et al., Automated early detection of diabetic retinopathy, Ophthalmology, 2010;6(117):1147–54.


EUROPEAN OPHTHALMIC REVIEW


107


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76