Name
Abstract | ||
|---|---|---|
Though graph cut based segmentation is a widely-used technique, it is known that segmentation of a thin, elongated structure is challenging due to the "shrinking problem". On the other hand, many segmentation targets in medical image analysis have such thin structures. Therefore, the conventional graph cut method is not suitable to be applied to them. In this study, we developed a graph cut segmentation method with novel Riemannian metrics. The Riemannian metrics are determined from the given "initial contour," so that any level-set surface of the distance transformation of the contour has the same surface area in the Riemannian space. This will ensure that any shape similar to the initial contour will not be affected by the shrinking problem. The method was evaluated with clinical CT datasets and showed a fair result in segmenting vertebral bones. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23626-6_68 | MICCAI (3) |
Keywords | Field | DocType |
thin structure,surface area,riemannian metrics,initial contour,3-d graph cut segmentation,level-set surface,riemannian space,novel riemannian metrics,graph cut segmentation method,segmentation target,conventional graph cut method,graph cut,riemannian geometry,spine,segmentation | Cut,Scale-space segmentation,Market segmentation,Pattern recognition,Segmentation,Computer science,Graph cut segmentation,Segmentation-based object categorization,Artificial intelligence,Riemannian geometry | Conference |
Volume | Issue | ISSN |
14 | Pt 3 | 0302-9743 |
Citations | PageRank | References |
4 | 0.45 | 7 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shouhei Hanaoka | 1 | 26 | 7.56 |
| Karl Fritscher | 2 | 19 | 3.49 |
| Martin Welk | 3 | 4 | 0.45 |
| Mitsutaka Nemoto | 4 | 46 | 8.42 |
| Yoshitaka Masutani | 5 | 145 | 30.52 |
| Naoto Hayashi | 6 | 20 | 6.38 |
| Kuni Ohtomo | 7 | 45 | 11.32 |
| Rainer Schubert | 8 | 79 | 12.86 |