Name
Title | ||
|---|---|---|
Joined fragment segmentation for fractured bones using GPU-accelerated shape-preserving erosion and dilation |
Abstract | ||
|---|---|---|
We propose a GPU accelerated segmentation framework, which mainly consists of normal-based erosion and record-based dilation, to automatically segment joined fragments for most cases. For the remaining cases, we introduce a random walk algorithm for segmentation with a few interactions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11517-019-02074-y | Medical & Biological Engineering & Computing |
Keywords | Field | DocType |
Joined fragment segmentation, Shape-preserving erosion and dilation, Volume data segmentation, Fractured bones, GPU acceleration | Voxel,Computer vision,Dilation (morphology),Fractured bone,Random walk,Segmentation,Artificial intelligence,Computed tomography,Connected-component labeling,Graphics processing unit,Mathematics | Journal |
Volume | Issue | ISSN |
58 | 1 | 1741-0444 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhang | 1 | 0 | 0.34 |
| Ruofeng Tong | 2 | 466 | 49.69 |
| Dan Song | 3 | 31 | 10.65 |
| Xiaobo Yan | 4 | 0 | 0.34 |
| Lanfen Lin | 5 | 78 | 24.70 |
| Jian Wu | 6 | 933 | 95.62 |