Name
Abstract | ||
|---|---|---|
This paper proposes an approach based on level sets to segment brain tumors from CT images. Combining edge information with region information dynamically, the novel method introduces a new energy function model, which will make the initial contour evolve towards the desirable boundary while not leak at weak edge positions. In addition, re-initialization of the evolving level set function is avoided by introducing a new simple regularization term, which can eliminate radical changes of level set function(LSF) far away from the contour, and make the LSF prone to be a signed distance function around the contour as well. Experimental results demonstrate that the proposed method performs well on CT images, and can segment brain tumors exactly. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33179-4_3 | ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT I |
Field | DocType | Volume |
Level set function,Active contour model,Computer vision,Pattern recognition,Segmentation,Computer science,Signed distance function,Level set,Regularization (mathematics),Function model,Artificial intelligence | Conference | 7431 |
Issue | ISSN | Citations |
PART 1 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| w ei zhenwen | 1 | 10 | 0.97 |
| Caiming Zhang | 2 | 446 | 88.19 |
| Xingqiang Yang | 3 | 4 | 3.07 |
| Xiaofeng Zhang | 4 | 78 | 8.90 |