Name
Abstract | ||
|---|---|---|
Deformable models and level set methods have been extensively investigated for computerized image segmentation. However, medical image segmentation is yet one of open challenges owing to diversified physiology, pathology, and imaging modalities. Existing level set methods suffer from some inherent drawbacks in face of noise, ambiguity, and inhomogeneity. It is also refractory to control level set segmentation that is dependent on image content and evolutional strategies. In this paper, a new level set formulation is proposed by using fuzzy region competition for selective image segmentation. It is able to detect and track the arbitrary combination of selected objects or image components. To the best of our knowledge, this new formulation should be one of the first proposals in a framework of region competition for selective segmentation. Experiments on both synthetic and real images validate its advantages in selective level set segmentation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ACCESS.2016.2590440 | IEEE ACCESS |
Keywords | Field | DocType |
Fuzzy control,image segmentation,level set methods,region competition,selective segmentation | Computer vision,Scale-space segmentation,Pattern recognition,Image texture,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Real image,Minimum spanning tree-based segmentation | Journal |
Volume | ISSN | Citations |
4 | 2169-3536 | 3 |
PageRank | References | Authors |
0.38 | 26 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing Nan Li | 1 | 240 | 18.77 |
| Jing Qin | 2 | 1109 | 95.43 |
| Rong Wang | 3 | 10 | 1.16 |
| Meng Wang | 4 | 2097 | 53.43 |
| Xuelong Li | 5 | 15049 | 617.31 |