Name
Title | ||
|---|---|---|
Label fusion method based on sparse patch representation for the brain MRI image segmentation. |
Abstract | ||
|---|---|---|
The multi-Atlas patch-based label fusion method (MAS-PBM) has emerged as a promising technique for the magnetic resonance imaging (MRI) image segmentation. The state-of-the-art MAS-PBM approach measures the patch similarity between the target image and each atlas image using the features extracted from images intensity only. It is well known that each atlas consists of both MRI image and labelled ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1049/iet-ipr.2016.0988 | IET Image Processing |
Keywords | Field | DocType |
biomedical MRI,feature extraction,image representation,image segmentation,medical image processing | Computer vision,Scale-space segmentation,Pattern recognition,Brain mri,Similarity measure,Segmentation,Fusion,Image segmentation,Feature extraction,Artificial intelligence,Majority rule,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 7 | 1751-9659 |
Citations | PageRank | References |
3 | 0.38 | 28 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Liu | 1 | 96 | 18.53 |
| Meng Yan | 2 | 5 | 1.08 |
| Enmin Song | 3 | 176 | 24.53 |
| Yuejing Qian | 4 | 8 | 1.47 |
| Xiangyang Xu | 5 | 61 | 7.92 |
| Renchao Jin | 6 | 30 | 8.83 |
| Lianghai Jin | 7 | 185 | 15.07 |
| Chih-Cheng Hung | 8 | 46 | 13.39 |