Name
Abstract | ||
|---|---|---|
The MR image segmentation is always a challenging problem because of the intensity inhomogeneity. Many existing methods don't reach their expected segmentations; besides their implementations are usually complicated. Therefore, we originally interleave the extended Otsu segmentation with bias field estimation in an energy minimization. Via our proposed method, the optimal segmentation and bias field estimation are achieved simultaneously throughout the reciprocal iteration. The results of our method not only satisfy the required classification via its applications in the synthetic and the real images, but also demonstrate that our method is superior to the baseline methods in accordance with the performance analysis of JS metrics. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transinf.2014EDL8042 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
intensity inhomogeneity, bias field estimation, Otsu segmentation, energy minimization | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Image segmentation,Artificial intelligence,Energy minimization | Journal |
Volume | Issue | ISSN |
E97D | 11 | 1745-1361 |
Citations | PageRank | References |
2 | 0.37 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoqi Xiong | 1 | 2 | 0.37 |
| Jingjing Gao | 2 | 96 | 9.73 |
| Chongjin Zhu | 3 | 15 | 1.66 |
| Yanling Li | 4 | 2 | 0.37 |
| Shu Zhang | 5 | 55 | 6.92 |
| Mei Xie | 6 | 56 | 13.64 |