Name
Abstract | ||
|---|---|---|
In this paper, we present a semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge. Firstly, some hard constraints are obtained according to the knowledge of liver characteristic appearance and anatomical location. Secondly, the energy function is constructed via knowledge based similarity measure. A path-based spatial connectivity measure is applied for robust regional properties. Finally, the image is interpreted as a graph, afterwards the segmentation problem s casted as an optimal cut on it, which can be computed through the existing max-flow algorithm. The model is evaluated on MICCAI 2007 liver segmentation challenge datasets and some other CT volumes from the hospital. The experimental results show its effectiveness and efficiency. (C) 2012 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.bspc.2012.04.005 | Biomedical Signal Processing and Control |
Keywords | DocType | Volume |
Liver CT segmentation,Graph-cut model,Domain knowledge | Journal | 7 |
Issue | ISSN | Citations |
6 | 1746-8094 | 13 |
PageRank | References | Authors |
0.73 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yufei Chen | 1 | 322 | 33.06 |
| Zhicheng Wang | 2 | 176 | 17.00 |
| Jinyong Hu | 3 | 15 | 1.10 |
| Weidong Zhao | 4 | 25 | 1.56 |
| Qidi Wu | 5 | 426 | 44.87 |