Distinguishing schizophrenic patients from healthy controls based on MRI data: A tensor linear discriminant approach

Recently, there are available laboratory procedures providing useful information to psychiatric diagnostic systems. In this paper, a tensor-based pattern recognition system was used to classify schizophrenic patients and healthy controls. The novel tensor approach is an extension of linear discriminant analysis (LDA). In this method, each subject´ structure MRI image was viewed as a tensor sample. After splitting samples into training and testing data, we obtained a series of projecting matrix through Tensor LDAalgorithm, and the feature matrix obtained can be used in the testing data to get the class labels. The performance of our system was tested by the leave-one-out cross-validation strategy. Experimental results showed that the sensitivity, specificity, and overall classification accuracy of our system were 86.36%, 94.44%, and 90%, respectively. Moreover, we compared the classification of Tensor LDAwith the traditional LDA. The results showed that the tensor method outperformed on this task than the traditional LDA.