Structural magnetic resonance imaging (SMRI) method has an intrinsic thirdorder tensor structure. Traditional vectorbased machine learning methods unfold the 3D images as vectors to carry on the modeling

which break the natural 3D structure of data so that some useful information underlying the neuroimaging data is missing. Therefore

a novel classification method based on the Support Tensor Machine (STM) is proposed to overcome these drawbacks. The T1 weighted MRI images are first preprocessed and segmented into gray matter (GM)

white matter (WM) and cerebrospinal fluid (CSF) regions using SPM8 tool. The third-order grey tensors are then constructed for each partition. Recursive feature elimination (RFE) method coupled with Support Tensor Machine (STM) is used to select the optimal features subset for classification using the STM-based classifier. The proposed algorithm perform the classification on four cases including the patients of Alzheimers disease and Mild Cognitive Impairment (including patients were converted to AD

MCI-C; and patients were not converted to AD

MCI-NC) and normal controls (NC)

10-fold cross validation is employed to assess the classification performance. In terms of AUC

classification accuracy

sensitivity and specificity

the case AD vs NC archive 99.1%

97.14%

95.71%

98.57% respectively; the case AD vs MCI archive 8829%

84.07%

78.57%

91.07% respectively; the case of MCI vs NC archive 89.18%

87.91%

93.75%

78.57% respectively; and the case MCI-C vs MCI-NC archive 87.5%

82.08%

80.36%

82.14% respectively The proposed method keeps the natural tensor structure of the original data and improves the performance of the classifier. The experimental results indicate that the proposed algorithm is effective for the diagnosis of Alzheimers disease.