Name
Abstract | ||
|---|---|---|
Due to the complexity of the brain image itself, the brain image segmentation technology has become a bottle for further application and development of the system. Considering the inconsistency of intensity, partial volume effect, and noise in medical images, this paper studies the brain image segmentation technology based on the multi-weight probability. The multi-weight probability method mainly models the data set with outliers and non-Gaussian noise. First, the probabilistic local ELM model is established. Based on this, the Parzen window method is used to establish the probability distribution of the local model, and then, the probability distribution is used as the weight to fuse. All local models are used to build a global robustness model. The method successfully applied the brain and UCI examples and compared with traditional ELM, regularized ELM, and robust ELM. The results show that the probability weight ELM shows better modeling performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2893275 | IEEE ACCESS |
Keywords | Field | DocType |
Brain image segmentation, multi-weight probability algorithm, partial volume effect, pixel correlation | Computer vision,Computer science,Image segmentation,Artificial intelligence,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Liu | 1 | 115 | 19.90 |
| Xiaoe Yu | 2 | 0 | 0.34 |
| Qianjin Feng | 3 | 271 | 43.84 |
| Wufan Chen | 4 | 511 | 59.06 |
| Gunasekaran Manogaran | 5 | 48 | 6.97 |