TY - GEN
T1 - Multi-Feature Kernel Discriminant Dictionary Learning for Classification in Alzheimer's Disease
AU - Li, Qing
AU - Wu, Xia
AU - Xu, Lele
AU - Yao, Li
AU - Chen, Kewei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - Classification of Alzheimer 's disease (AD) from normal control (NC) is important for disease abnormality identification and intervention. The current study focused on distinguishing AD from NC based on the multi-feature kernel supervised within-class-similarity discriminative dictionary learning algorithm (MKSCDDL) we introduced previously, which has been derived outperformance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir-PET data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database were adopted for classification between AD and NC. Adopting MKSCDDL, not only the classification accuracy achieved 98.18% for AD vs. NC, which were superior to the results of some other state-of-the-art approaches (MKL, JRC, and mSRC), but also testing time achieved outperforming results. The MKSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.
AB - Classification of Alzheimer 's disease (AD) from normal control (NC) is important for disease abnormality identification and intervention. The current study focused on distinguishing AD from NC based on the multi-feature kernel supervised within-class-similarity discriminative dictionary learning algorithm (MKSCDDL) we introduced previously, which has been derived outperformance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir-PET data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database were adopted for classification between AD and NC. Adopting MKSCDDL, not only the classification accuracy achieved 98.18% for AD vs. NC, which were superior to the results of some other state-of-the-art approaches (MKL, JRC, and mSRC), but also testing time achieved outperforming results. The MKSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.
KW - Alzheimer's disease (AD)
KW - Discriminant dictionary
KW - Multi-modality Neuroimaging data
KW - Multiple kernel learning
UR - https://www.scopus.com/pages/publications/85048272139
U2 - 10.1109/DICTA.2017.8227467
DO - 10.1109/DICTA.2017.8227467
M3 - Conference contribution
AN - SCOPUS:85048272139
T3 - DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications
SP - 1
EP - 6
BT - DICTA 2017 - 2017 International Conference on Digital Image Computing
A2 - Guo, Yi
A2 - Li, Hongdong
A2 - Cai, Weidong Tom
A2 - Murshed, Manzur
A2 - Wang, Zhiyong
A2 - Gao, Junbin
A2 - Feng, David Dagan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017
Y2 - 29 November 2017 through 1 December 2017
ER -