Name
Title | ||
|---|---|---|
Interleaved K-Nn Classification And Bias Field Estimation For Mr Image With Intensity Inhomogeneity |
Abstract | ||
|---|---|---|
k-NN classification has been applied to classify normal tissues in MR images. However, the intensity inhomogeneity of MR images forces conventional k-NN classification into significant misclassification errors. This letter proposes a new interleaved method, which combines k-NN classification and bias field estimation in an energy minimization framework, to simultaneously overcome the limitation of misclassifications in conventional k-NN classification and correct the bias field of observed images. Experiments demonstrate the effectiveness and advantages of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transinf.E97.D.1011 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
intensity inhomogeneity, bias field estimation, k-NN classification, minimizing energy | Computer vision,Pattern recognition,Computer science,Artificial intelligence,Bias field | Journal |
Volume | Issue | ISSN |
E97D | 4 | 1745-1361 |
Citations | PageRank | References |
2 | 0.41 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Gao | 1 | 96 | 9.73 |
| Mei Xie | 2 | 56 | 13.64 |
| Ling Mao | 3 | 2 | 0.41 |