Abstract
A fully automated digital image processing system, which provides an objective and repeatable way to quantify microaneurysms in digitised fluorescein angiograms, has been developed. The automated computer processing includes registration of same-eye retinal images for serial studies, cutting of regions-of-interest centred on the fovea, the detection of microaneurysms and the comparison of serial images for microaneurysm turnover. The microaneurysm detector was trained against a database of 68 images of patients with diabetes containing 394 true microaneurysms, as identified by an ophthalmologist. The microaneurysm detector achieved 82% sensitivity with 2.0 false-positives per image. An independent test set, comprising 20 images containing 297 true microaneurysms, was used to compare the microaneurysm detector with clinicians. The microaneurysm detector achieved a sensitivity of 82% for 5.7 false-positives per image, whereas the clinician receiver-operator-characteristic (ROC) curve gives 3.2 false-positives per image at a sensitivity of 82%. It is concluded that the computer system can reliably detect microaneurysms. The advantages of the computer system include objectivity, repeatability, speed and full automation.
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Kohner EM, Sleightholm M, and KROC Collaborative Study Group. Does microaneurysm count reflect severity of early diabetic retinopathy? Ophthalmology 1986;93:586–9.
Klein R, Meuer SM, Moss SE, Klein BEK . The relationship of retinal microaneurysm counts to the 4-year progression of diabetic retinopathy. Arch Ophthalmol 1989;107:1780–5.
Lay B, Baudoin C, Klein JC . Automatic detection of microaneurysms in retinopathy fluoro-angiogram. Proc SPIE 1983;432:165–73.
Spencer T, Phillips RP, Sharp PF, Forrester JV . Automated detection and quantification of microaneurysms in fluorescein angiograms. Graefes Arch Clin Exp Ophthalmol 1992;230:36–41.
Spencer T, Olson JA, McHardy KC, Sharp PF, Forrester JV . An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comp Biomed Res 1996;29:284–302.
Cideciyan AV, Jacobson SG, Kemp CM, Knighton RW, Nagel JH . Registration of high resolution images of the retina. Proc SPIE 1992;1652:310–22.
Frame AJ, Cree MJ, Olson JA, Undrill PE, McHardy KC, Sharp PF, Forrester JV . Computer-based classification of retinal microaneurysms. In: Proceedings of the 2nd International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, Plymouth, UK, 1996:50–6.
Metz CE . ROC methodology in radiologic imaging. Invest Radiol 1986;21:720–33.
Kohner EM, Dollery CT . The rate of formation and disappearance of microaneurysms in diabetic retinopathy. Eur J Clin Invest 1970;1:167–71.
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Cree, M., Olson, J., McHardy, K. et al. A fully automated comparative microaneurysm digital detection system. Eye 11, 622–628 (1997). https://doi.org/10.1038/eye.1997.166
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DOI: https://doi.org/10.1038/eye.1997.166
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