Name
Abstract | ||
|---|---|---|
Liver segmentation in MR Image is the first step of our automated liver perfusion analysis project. Traditional Level Set methods and active contours were often used to segment the liver, but the results were not always promising due to noise and the low gradient response on the liver boundary. In this paper we propose a novel level set based variational approach that incorporates shape prior knowledge into the improved Chan-Vese's model [1] which can overcome the leakage and over-segmentation problems. The experiments are taken on abdomen MRI series and the results reveal that our improved level set based shape prior method can segment liver shape precisely and a refined liver perfusion curve without respiration affection can be achieved. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-79982-5_17 | MIAR |
Keywords | Field | DocType |
novel level,liver segmentation,mri images,improved chan-vese,prior knowledge,segment liver shape,improved level,liver boundary,novel level set,refined liver perfusion curve,automated liver perfusion analysis,shape prior method,level set,level set method,active contour | Computer vision,Pattern recognition,Segmentation,Level set,Liver perfusion,Artificial intelligence,Abdomen MRI,Mathematics | Conference |
Volume | ISSN | Citations |
5128 | 0302-9743 | 7 |
PageRank | References | Authors |
0.57 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kan Cheng | 1 | 16 | 2.37 |
| Lixu Gu | 2 | 230 | 35.28 |
| Jianghua Wu | 3 | 7 | 0.57 |
| Wei Li | 4 | 310 | 71.89 |
| Jianrong Xu | 5 | 54 | 5.65 |