Name
Abstract | ||
|---|---|---|
Centerline is generally used to measure topological and morphological parameters of blood vessels, which is pivotal for the quantitative analysis of vascular diseases. However, previous centerline extraction methods have two drawbacks on complex blood vessels, represented as the failure on ring-like structures and the existing of multi-voxel width. In this paper, we propose a monocentric centerline extraction method for ring-like blood vessels, which consists of three components. First, multiple centerlines are generated from the seed points that are chosen by randomly sprinkling points on blood vessel data. Second, multi-centerline fusion is used to repair the notches of centerlines on ring-like vessels, and the local maximum of distance from oundary is employed to remedy the missing centerline points. Finally, monocentric processing is devised to keep the vascular centerline with single voxel width. We compared the proposed method with Wan et al.'s method and topological thinning on five groups of data including synthesized vascular datasets and MR brain images. The result showed the proposed method performed better than the two contrast methods both by visual inspection and by quantitative assessment, which demonstrated the performance of the proposed method on ring-like blood vessels as well as the elimination of multi-voxel width points. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11517-017-1717-8 | Med. Biol. Engineering and Computing |
Keywords | Field | DocType |
Centerline extraction,Blood vessels,Information fusion,Monocentric processing | Voxel,Computer vision,Artificial intelligence,Quantitative assessment,Information fusion,Mathematics,Blood vessel | Journal |
Volume | Issue | ISSN |
56 | 4 | 1741-0444 |
Citations | PageRank | References |
1 | 0.34 | 22 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengjun Zhao | 1 | 18 | 4.37 |
| Sun Feifei | 2 | 1 | 0.34 |
| Hou Yuqing | 3 | 1 | 3.05 |
| Chen Yanrong | 4 | 1 | 0.34 |
| Dongmei Chen | 5 | 9 | 4.86 |
| Cao Xin | 6 | 5 | 4.51 |
| Yuqing Hou | 7 | 3 | 2.40 |
| Bin Wang | 8 | 1788 | 246.68 |
| Xiaowei He | 9 | 10 | 6.79 |
| Liang Jimin | 10 | 1 | 0.34 |