Name
Title | ||
|---|---|---|
Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework. |
Abstract | ||
|---|---|---|
•We propose a multiplex vessel partition method that exploits the relation between the real and imaginary parts of the complex Gabor filter response to decompose the vessel pixel pattern into a number of homogeneous patterns, making each of them easier to be discriminated from the non-vessel pixels.•We propose a funnel-structured vessel segmentation framework to reclassify the uncertain samples caused by imperfect data partition in the dividing phase, which further enhances the complexity and discriminative capability of the whole decision model.•Quantitatively and qualitatively analyses of our segmented performance on three diverse databases show the superiority of the proposed framework on vessel segmentation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.sigpro.2019.06.018 | Signal Processing |
Keywords | Field | DocType |
Retinal vessel segmentation,Divide-and-conquer,Multiplex vessel partition,Funnel-structured classification framework | Vessel segmentation,Mathematical optimization,Pattern recognition,Division (mathematics),Artificial intelligence,Decision model,Pixel,Divide and conquer algorithms,Retinal,Funnel,Discriminative model,Mathematics | Journal |
Volume | ISSN | Citations |
165 | 0165-1684 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohong Wang | 1 | 9 | 1.49 |
| Xudong Jiang | 2 | 1885 | 117.85 |