Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.
OBJECTIVE
This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification.


METHODS
Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier.


RESULTS
The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%.


CONCLUSIONS
Our method performs better than Gorji's approach and five other state-of-the-art approaches.
Shui-Hua
SH
Wang
Sidan
S
Du
Yin
Y
Zhang
Preetha
P
Phillips
Le-Nan
LN
Wu
Xian-Qing
XQ
Chen
School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023,. China.
Yu-Dong
YD
Zhang
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
Alzheimer’s disease; linear regression classification; pseudo Zernike moment.