Name
Abstract | ||
|---|---|---|
The optic cup segmentation is critical for automated cup-to-disk ratio measurement, and hence computer-aided diagnosis of glaucoma. In this paper, we propose a novel sector-based method for optic cup segmentation. The method comprises two parts: intensity-based cup segmentation with shape constraints and blood vessel-based refinement. The initial estimation of the cup is obtained by applying a statistical deformable model on the vessel free image. At the same time, blood vessels within the optic disk are extracted, after which vessel bendings and vessel boundaries in the nasal side are located. Subsequently, these key points in the blood vessels are used to fine tune the cup. The algorithm is evaluated on 650 fundus images from the ORIGA(-light) database. Experimental results show that the Dice coefficient for the optic cup segmentation can be as high as 0.83, which outperforms other existing methods. The results demonstrate good potential for the proposed method to be used in automated optic cup segmentation and glaucoma diagnosis. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EMBC.2012.6346214 | EMBC |
Keywords | Field | DocType |
biomechanics,vessel bending,diseases,sector-based optic cup segmentation,blood vessel prior,biomedical optical imaging,statistical deformable model,optic disk,sector-based method,vessel free image,nasal side,blood vessels,image segmentation,blood vessel-based refinement,origalight database,glaucoma diagnosis,bending,dice coefficient,vision defects,vessel boundary,intensity-based cup segmentation,medical image processing | Computer vision,Glaucoma,Segmentation,Sørensen–Dice coefficient,Computer science,Fundus (eye),Optic disk,Image segmentation,Artificial intelligence,Optic cup (anatomical),Prior probability | Conference |
Volume | ISSN | ISBN |
2012 | 1557-170X | 978-1-4577-1787-1 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengshou Yin | 1 | 125 | 9.66 |
| Jiang Liu | 2 | 299 | 42.50 |
| Damon Wing Kee Wong | 3 | 434 | 37.78 |
| Ngan Meng Tan | 4 | 175 | 15.21 |
| Jun Cheng | 5 | 214 | 20.65 |
| Ching Yu Cheng | 6 | 103 | 6.95 |
| Yih Chung Tham | 7 | 0 | 0.34 |
| Tien Yin Wong | 8 | 59 | 6.85 |