Name
Abstract | ||
|---|---|---|
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6091472 | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
biomedical imaging,optical imaging,histograms,measurement,image quality | Computer vision,Histogram,Grading (education),Medical imaging,Computer science,Image quality,Algorithm,Retinal image,Preprocessor,Artificial intelligence,Reference standards,Fundus image | Conference |
Volume | ISSN | Citations |
2011 | 1557-170X | 7 |
PageRank | References | Authors |
0.68 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Hunter | 1 | 175 | 11.31 |
| James A Lowell | 2 | 13 | 1.13 |
| Maged Habib | 3 | 7 | 1.02 |
| Bob Ryder | 4 | 7 | 0.68 |
| Ansu Basu | 5 | 140 | 8.67 |
| David Steel | 6 | 7 | 0.68 |