TY - JOUR
T1 - Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection
AU - Liu, Luyan
AU - Wang, Qian
AU - Adeli, Ehsan
AU - Zhang, Lichi
AU - Zhang, Han
AU - Shen, Dinggang
N1 - Funding Information:
This work is supported by National Key Research and Development Program of China ( 2017YFC0107600 ), National Natural Science Foundation of China (NSFC) Grants (Nos. 61473190 , 61401271 , 81471733 ), Science and Technology Commission of Shanghai Municipality ( 16511101100 , 16410722400 ). This work was also supported in part by NIH grants ( EB006733 , EB008374 , AG041721 , AG049371 , AG042599 , and EB022880 ).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/7
Y1 - 2018/7
N2 - Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis (ICCA) feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. To state succinctly, we first extract the feature vectors from the gray matter and the white matter tissues separately, represented as insights of two different anatomical feature spaces for the subject's brain. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other. In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods.
AB - Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis (ICCA) feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. To state succinctly, we first extract the feature vectors from the gray matter and the white matter tissues separately, represented as insights of two different anatomical feature spaces for the subject's brain. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other. In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods.
KW - Diagnosis
KW - Feature selection
KW - Imaging biomarkers
KW - Iterative canonical correlation analysis
KW - Parkinson's disease
UR - http://www.scopus.com/inward/record.url?scp=85046012624&partnerID=8YFLogxK
U2 - 10.1016/j.compmedimag.2018.04.002
DO - 10.1016/j.compmedimag.2018.04.002
M3 - Article
C2 - 29702348
AN - SCOPUS:85046012624
SN - 0895-6111
VL - 67
SP - 21
EP - 29
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
ER -