Name
Abstract | ||
|---|---|---|
Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain images. The segmentation of magnetic resonance image (MRI) is a challenging problem that has received an enormous amount of attention lately. In this paper, we propose a simple and effective segmentation method combining watershed algorithm and normalized cuts (CWNC) for MR brain images. An initial partitioning of the MRI into primitive regions is set by applying the watershed transform. The latter process uses a region similarity graph representation of the image regions. And then the graph is segmented by normalized cuts algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BIBE.2008.4696839 | BIBE |
Keywords | Field | DocType |
real mr images,computer aided medical image processing,region similarity graph representation,watershed algorithm,image segmentation,simulated mr images,normalized cuts algorithm,graph theory based algorithm,primitive region,biomedical mri,brain,clinical analysis,watershed transform,graph theory,magnetic resonance brain image segmentation,medical image processing,magnetic resonance image,magnetic resonance imaging,graph representation,magnetic resonance,pixel,biomedical imaging,brain imaging | Scale-space segmentation,Medical imaging,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Graph theory,Computer vision,Pattern recognition,Segmentation,Algorithm,Connected-component labeling,Graph (abstract data type),Machine learning | Conference |
ISSN | ISBN | Citations |
2471-7819 | 978-1-4244-2845-8 | 0 |
PageRank | References | Authors |
0.34 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianzhong Wang | 1 | 214 | 17.72 |
| Di Liu | 2 | 37 | 5.69 |
| Lili Dou | 3 | 2 | 0.81 |
| Baoxue Zhang | 4 | 142 | 11.16 |
| Jun Kong | 5 | 158 | 14.14 |
| Yinghua Lu | 6 | 30 | 10.56 |