Name
Abstract | ||
|---|---|---|
This paper presents a new method for classification of retina into glaucoma and non-glaucoma cases based on optical coherence tomography angiogram (OCTA). The key idea here is to analyze the retinal microvasculature in the optic disc area of an enface OCTA for glaucoma classification. To facilitate this analysis, we propose a way to extract a so-called "optic disc microvasculature" region and then propose several features that will be extracted from this microvasculature region. A machine classifier is then trained using the designated features and subsequently used to classify the OCTA data. We show that our proposed approach works well on the tested dataset. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8036895 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Optical coherence tomography,Glaucoma,Computer science,Retina,Optic disc,Artificial intelligence,Retinal | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ee Ping Ong | 1 | 313 | 33.36 |
| Jun Cheng | 2 | 214 | 20.65 |
| Damon Wing Kee Wong | 3 | 434 | 37.78 |
| Jiang Liu | 4 | 335 | 34.30 |
| Elton L. T. Tay | 5 | 0 | 0.34 |
| Leonard W. L. Yip | 6 | 0 | 1.01 |