Name
Abstract | ||
|---|---|---|
Liver segmentation in computerized tomography (CT) images has been widely studied in recent years, of which the graph cut models demonstrate a great potential with the advantage of global optima and practical efficiency. In this paper, a graph-cut based model for liver CT segmentation is presented. The image is interpreted as a graph, that the segmentation problem is then casted as an optimal cut on the graph. An energy function is then formulated for minimization, which combines both regional properties and boundary smoothness. The prior knowledge on liver is unified into the energy function via fuzzy similarity measure. Finally, the optimal cut can be computed through the max-flow algorithm. Experiments on a variety of CT images show its effectiveness and efficiency. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-28557-8_20 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
optimal cut,energy function,liver segmentation,liver ct segmentation,graph cut model,boundary smoothness,ct image,practical efficiency,segmentation problem,computerized tomography | Cut,Scale-space segmentation,Pattern recognition,Segmentation,Segmentation-based object categorization,Tomography,Image segmentation,Minification,Artificial intelligence,Radiology,Smoothness,Medicine | Conference |
Volume | Issue | ISSN |
7029 LNCS | null | 16113349 |
Citations | PageRank | References |
2 | 0.37 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yufei Chen | 1 | 322 | 33.06 |
| Weidong Zhao | 2 | 25 | 3.05 |
| Qidi Wu | 3 | 426 | 44.87 |
| Zhicheng Wang | 4 | 176 | 17.00 |
| Jinyong Hu | 5 | 15 | 1.10 |