Name
Abstract | ||
|---|---|---|
This paper introduces a new approach to segment vessels from medical images using the fast marching method. Our approach relies on an iterative scheme: Starting from a given start point and initial direction, the optimal path within a circular region of interest (ROI) around this point is found using the fast marching method and a combination of different speed functions. Besides using speed functions based on a vesselness measure and the vessel radius, we introduce a directional speed function which prefers directions close to the predicted direction. The end point of the detected path is then used as the new start point to find again the optimal path within a new ROI centered around this point. This procedure is repeated until the user-specified end point is reached, or some other termination criterion is satisfied. The final result is the concatenation of the sequence of paths of the individual ROIs. Our approach has been applied to synthetic and real datasets. The experiments show that our approach is not only more efficient than a previous fast marching approach but also produces better results when dealing with short cuts and crossings in the segmentation of long vessels. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911483 | Proceedings of SPIE |
Keywords | Field | DocType |
Vessel segmentation,Fast marching,Directional speed function | Computer vision,Vessel segmentation,Start point,Segmentation,Fast marching method,End point,Concatenation,Artificial intelligence,Region of interest,Physics | Conference |
Volume | ISSN | Citations |
8314 | 0277-786X | 2 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| wei liao | 1 | 2 | 0.71 |
| Stefan Wörz | 2 | 256 | 32.58 |
| K. Rohr | 3 | 138 | 14.52 |