Name
Title | ||
|---|---|---|
An iterative possibilistic knowledge diffusion approach for blind medical image segmentation. |
Abstract | ||
|---|---|---|
•A novel region-growing segmentation method based on possibilistic theory is proposed.•Region-growing process is iteratively performed at the possibilistic knowledge representation level.•Possibility theory allows adequate semantic knowledge modeling without huge constraints.•Validation is done in the context of pixel classification using both real and synthetic data.•Proposed approach shows remarkable stable behaviour during quantitative assessment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2018.01.024 | Pattern Recognition |
Keywords | Field | DocType |
Possibilistic knowledge representation,Knowledge diffusion modeling,Iterative segmentation,Region growing,Image segmentation,Mammographic medical images | Anisotropic diffusion,Pattern recognition,Segmentation,Image segmentation,Smoothing,Artificial intelligence,Pixel,Region growing,Real image,Knowledge modeling,Mathematics | Journal |
Volume | Issue | ISSN |
78 | C | 0031-3203 |
Citations | PageRank | References |
2 | 0.37 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Imene Khanfir Kallel | 1 | 13 | 2.35 |
| Shaban Almouahed | 2 | 21 | 6.73 |
| Basel Solaiman | 3 | 127 | 35.05 |
| Éloi Bossé | 4 | 386 | 26.19 |