Name
Abstract | ||
|---|---|---|
This paper introduces an algorithm for the automated diagnosis of referable maculopathy in retinal images for diabetic retinopathy screening. Referable maculopathy is a potentially sight-threatening condition requiring immediate referral to an ophthalmologist from the screening service, and therefore accurate referral is extremely important. The algorithm uses a pipeline of detection and filtering of "peak points" with strong local contrast, segmentation of candidate lesions, extraction of features and classification by a multilayer perceptron. The optic nerve head and fovea are detected, so that the macula region can be identified and scanned. The algorithm is assessed against a reference standard database drawn from the Birmingham City Hospital (UK) diabetic retinopathy screening programme, against two possible modes of use: independent screening, and pre-filtering to reduce human screener workload. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6090914 | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
diabetes,sensitivity,multilayer perceptron,feature extraction,image segmentation | Retinopathy,Ophthalmology,Maculopathy,Optometry,Medicine,Reference standards,Optic nerve,Diabetic retinopathy screening,Referral | Conference |
Volume | ISSN | Citations |
2011 | 1557-170X | 6 |
PageRank | References | Authors |
0.45 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Hunter | 1 | 140 | 9.10 |
| James A Lowell | 2 | 13 | 1.13 |
| Bob Ryder | 3 | 6 | 0.45 |
| Ansu Basu | 4 | 140 | 8.67 |
| David Steel | 5 | 155 | 11.88 |