TY - JOUR
T1 - Application of stationary wavelet entropy in pathological brain detection
AU - Wang, Shuihua
AU - Du, Sidan
AU - Atangana, Abdon
AU - Liu, Aijun
AU - Lu, Zeyuan
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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.
AB - 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.
KW - Discrete wavelet transform
KW - Magnetic resonance imaging
KW - Pathological brain detection
KW - Stationary wavelet entropy
KW - Wavelet energy
KW - Wavelet entropy
UR - http://www.scopus.com/inward/record.url?scp=84960340430&partnerID=8YFLogxK
U2 - 10.1007/s11042-016-3401-7
DO - 10.1007/s11042-016-3401-7
M3 - Article
AN - SCOPUS:84960340430
SN - 1380-7501
VL - 77
SP - 3701
EP - 3714
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 3
ER -