Name
Abstract | ||
|---|---|---|
Labeling brain images as healthy or pathological cases is an important procedure for medical diagnosis. Therefore, we proposed a novel image feature, stationary wavelet entropy (SWE), to extract brain image features. Meanwhile, we replaced the feature extraction procedure in state-of-the-art approaches with the proposed SWE. We found the classification performance improved after replacing wavelet entropy (WE), wavelet energy (WN), and discrete wavelet transform (DWT) with the proposed SWE. This proposed SWE is superior to WE, WN, and DWT. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-016-3401-7 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Magnetic resonance imaging, Stationary wavelet entropy, Pathological brain detection, Wavelet entropy, Wavelet energy, Discrete wavelet transform | Harmonic wavelet transform,Pattern recognition,Lifting scheme,Computer science,Second-generation wavelet transform,Discrete wavelet transform,Artificial intelligence,Stationary wavelet transform,Wavelet packet decomposition,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
77 | 3 | 1573-7721 |
Citations | PageRank | References |
17 | 0.60 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuihua Wang | 1 | 1564 | 87.49 |
| Sidan Du | 2 | 65 | 5.35 |
| Abdon Atangana | 3 | 71 | 12.66 |
| Aijun Liu | 4 | 66 | 4.18 |
| Zeyuan Lu | 5 | 17 | 0.60 |