Name
Title | ||
|---|---|---|
Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm. |
Abstract | ||
|---|---|---|
Retinal vessel segmentation constitutes an essential part of computer-assisted tools for the diagnosis of ocular diseases. In this study, the authors propose an unsupervised retinal blood vessels segmentation approach based on the elite-guided multi-objective artificial bee colony (EMOABC) algorithm. The proposed method exploits several criteria simultaneously to improve the accuracy of the segmen... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-ipr.2018.5425 | IET Image Processing |
Keywords | Field | DocType |
ant colony optimisation,biomedical optical imaging,blood vessels,diseases,eye,image enhancement,image segmentation,medical image processing,optimisation | Convergence (routing),Computer vision,Artificial bee colony algorithm,Vessel segmentation,Pattern recognition,Segmentation,Artificial intelligence,Retinal,Thresholding,Mathematics,Speedup,Metaheuristic | Journal |
Volume | Issue | ISSN |
12 | 12 | 1751-9659 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bilal Khomri | 1 | 1 | 0.35 |
| Argyrios Christodoulidis | 2 | 10 | 0.89 |
| Leila Djerou | 3 | 1 | 0.35 |
| Mohamed Chaouki Babahenini | 4 | 9 | 3.87 |
| Farida Cheriet | 5 | 482 | 61.48 |