Name
Title | ||
|---|---|---|
Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure. |
Abstract | ||
|---|---|---|
In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2017.2776126 | IEEE Access |
Keywords | Field | DocType |
Lighting,Image quality,Retina,Image segmentation,Image resolution,Indexes | Computer vision,Computer science,Naturalness,Image quality,Fundus (eye),Image segmentation,Feature set,Artificial intelligence,Classifier (linguistics),Image resolution,Medical diagnosis | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 3 |
PageRank | References | Authors |
0.41 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Shao | 1 | 603 | 72.75 |
| Yan Yang | 2 | 3 | 0.75 |
| Qiuping Jiang | 3 | 148 | 22.19 |
| Gangyi Jiang | 4 | 865 | 105.98 |
| Yo-Sung Ho | 5 | 1288 | 146.57 |