Name
Abstract | ||
|---|---|---|
In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures on 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D regions are identified in order to attribute an influence zone only to relevant minima of the image. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. The goal of the decision stage is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. Upon completion of the segmentation, the outcome of the preceding process is presented to the user for manual selection of the structures of interest (SOI). Results of this approach are described and illustrated with examples of segmented 3D MRIs of the human head. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1117/12.387690 | Proceedings of SPIE |
Keywords | Field | DocType |
3D MRI segmentation,3D watersheds,marker extraction | Adjacency list,Computer vision,Scale-space segmentation,Segmentation,Edge detection,Mathematical morphology,Segmentation-based object categorization,Image processing,Image segmentation,Artificial intelligence,Geography | Conference |
Volume | ISSN | Citations |
3979 | 0277-786X | 6 |
PageRank | References | Authors |
0.54 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gloria Bueno | 1 | 267 | 23.66 |
| Olivier Musse | 2 | 65 | 6.81 |
| Fabrice Heitz | 3 | 401 | 59.55 |
| Jean-Paul Armspach | 4 | 221 | 26.60 |