Name
Abstract | ||
|---|---|---|
Digital (binary) image segmentation is a critical step in most image processing protocols, especially in medical imaging where accurate and fast segmentation and classification are a challenging issue. In this paper we present a fast relaxation algorithm to minimize an anistropic Mumford-Shah energy functional for piecewise constant approximation of corrupted data. The algorithm is tested with synthetic phantoms and some CT images of the abdomen. Our results are finally compared with manual segmentations in order to validate the proposed model. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02172-5_42 | IbPRIA |
Keywords | Field | DocType |
corrupted data,challenging issue,image processing protocol,critical step,fast anisotropic mumford-shah functional,anistropic mumford-shah energy,ct image,manual segmentation,fast segmentation,fast relaxation algorithm,image segmentation,binary image,image processing | Mumford–Shah functional,Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Image texture,Binary image,Segmentation-based object categorization,Image processing,Image segmentation,Artificial intelligence | Conference |
Volume | ISSN | Citations |
5524 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. F. Garamendi | 1 | 3 | 0.79 |
| N. Malpica | 2 | 45 | 7.25 |
| E. Schiavi | 3 | 6 | 2.29 |