Name
Abstract | ||
|---|---|---|
Supervoxel segmentation has become an essential tool to medical image analysis for three-dimension MR image. However, in no consideration of the intensity inhomogeneity in 2D/3D MR image, the state-of-the-art supervoxel segmentation methods do not satisfy the further analysis, such as tissue classification according to intensity feature. In order to overcome the above-mentioned issues, we propose a modified supervoxel segmentation method for three-dimension MR image, which integrates the bias field into the weighted distance metric to determine the nearest cluster center. The supervoxel segmentation and bias correction can be simultaneously completed in our method. Especially, the bias corrected image lays the foundation for the supervoxel classification in accordance with the intensity feature. The experimental results and quantitative evaluation showed that the supervoxels obtained by our method are adherence to the MR tissue boundaries, and the bias corrected image is positive for the intensity feature extraction. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s11063-017-9704-5 | Neural Processing Letters |
Keywords | Field | DocType |
Supervoxel,Segmentation,Bias correction,Intensity inhomogeneity | Computer vision,Weighted distance,Pattern recognition,Segmentation,Feature extraction,Bias correction,Artificial intelligence,Mathematics,Bias field | Journal |
Volume | Issue | ISSN |
48 | 1 | 1370-4621 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Gao | 1 | 1 | 0.35 |
| Xin Dai | 2 | 1 | 0.69 |
| Chongjin Zhu | 3 | 15 | 1.66 |
| Jie-Zhi Cheng | 4 | 102 | 13.00 |
| Xiaoguang Tu | 5 | 11 | 8.10 |
| Dai-Qiang Chen | 6 | 92 | 8.35 |
| Bin Sun | 7 | 4 | 1.07 |
| Yachun Gao | 8 | 1 | 0.35 |
| Mei Xie | 9 | 1 | 1.36 |