Assisted Magnetic Resonance Imaging Diagnosis for Alzheimer’s Disease Based on Kernel Principal Component Analysis and Supervised Classification Schemes
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초록
Alzheimer’s disease (AD) is an insidious and degenerative neurological disease. It is a new topic for ADpatients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored atpresent. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principalcomponent analysis (KPCA) is used to extract features of brain gray matter images. Finally supervisedclassification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classifythe above features. Experimental results by means of AD program Alzheimer’s Disease NeuroimagingInitiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients withmild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist thediagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classificationresults on KPCA are improved by 2%–6% among which the best result can reach 84%. It indicates that KPCAalgorithm for feature extraction is more abundant and complete than PCA.
Abstract
Alzheimer’s disease (AD) is an insidious and degenerative neurological disease. It is a new topic for ADpatients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored atpresent. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principalcomponent analysis (KPCA) is used to extract features of brain gray matter images. Finally supervisedclassification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classifythe above features. Experimental results by means of AD program Alzheimer’s Disease NeuroimagingInitiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients withmild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist thediagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classificationresults on KPCA are improved by 2%–6% among which the best result can reach 84%. It indicates that KPCAalgorithm for feature extraction is more abundant and complete than PCA.
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