Several anatomical magnetic resonance imaging (MRI) markers for Alzheimer's disease (AD) have been identified. Cortical gray matter volume, cortical thickness, and subcortical volume have been used successfully to assist the diagnosis of Alzheimer's disease including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Currently, these anatomical MRI measures have mainly been used separately. Thus, the full potential of anatomical MRI scans for AD diagnosis might not yet have been used optimally. Meanwhile, most studies currently only focused on morphological features of regions of interest (ROIs) or interregional features without considering the combination of them. To further improve the diagnosis of AD, we propose a novel approach of extracting ROI features and interregional features based on multiple measures from MRI images to distinguish AD, MCI (including MCIc and MCInc), and health control (HC). First, we construct six individual networks based on six different anatomical measures (i.e., CGMV, CT, CSA, CC, CFI, and SV) and Automated Anatomical Labeling (AAL) atlas for each subject. Then, for each individual network, we extract all node (ROI) features and edge (interregional) features, and denoted as node feature set and edge feature set, respectively. Therefore, we can obtain six node feature sets and six edge feature sets from six different anatomical measures. Next, each feature within a feature set is ranked by -score in descending order, and the top ranked features of each feature set are applied to MKBoost algorithm to obtain the best classification accuracy. After obtaining the best classification accuracy, we can get the optimal feature subset and the corresponding classifier for each node or edge feature set. Afterwards, to investigate the classification performance with only node features, we proposed a weighted multiple kernel learning (wMKL) framework to combine these six optimal node feature subsets, and obtain a combined classifier to perform AD classification. Similarly, we can obtain the classification performance with only edge features. Finally, we combine both six optimal node feature subsets and six optimal edge feature subsets to further improve the classification performance. Experimental results show that the proposed method outperforms some state-of-the-art methods in AD classification, and demonstrate that different measures contain complementary information.