A classification method based on the third-order tensors of brain structural magnetic resonance images is proposed to automatically identify Alzheimers disease and mild cognitive impairment. Brain structural magnetic resonance images from 70 AD patients

112 MCI patients (included patients were converted to AD during follow-up

MCI-C: MCI Converters and patients were not converted to AD during follow-up

MCI-NC: MCI Non-converters) and 70 NCs (normal controls) are collected. The third-order tensors are obtained by extracting image intensity of each voxel of gray matter. In order to obtain the independent components of the third-order tensors

independent component analysis (ICA) is applied. Then

support tensor machine (STM) and recursive feature elimination (RFE) are used to reduce features dimensions and determine dominate features for classification. Finally

the classification of four groups

such as AD-NC

MCI-NC

AD-MCI

MCI-C--MCI-NC

is implemented by using 7-fold cross-validation method. In addition

basic information and cognitive scores are combined with the thirdorder tensor for classification. It is proved that basic information

cognitive scores and image intensity of brain gray matter provide complementary information

which is helpful to improve the classification effect. The experiment results show that this method can achieve excellent classification effect

which contributes to the diagnosis and treatment of Alzheimers disease and mild cognitive impairment.