Application of 3D Whole-Brain Texture Analysis and the Feature Selection Method Based on within-Class Scatter in the Classification and Diagnosis of Alzheimer’s Disease

Background Patients with mild cognitive impairment (MCI) are a high-risk group for Alzheimer’s disease (AD). Thus, a reliable prediction of the conversion from MCI to AD based on three-dimensional (3D) texture features of MRI images could help doctors in developing effective treatment protocols. Methods The 3D texture features of the whole-brain were deduced based on the gray-level co-occurrence matrix. Then, the embedded feature selection method based on least squares loss and within-class scatter (LSWCS) was employed to select the optimal subsets of features that were used for binary classification (AD, MCI_C, MCI_S, normal control in pairs) based on SVM. A tenfold cross validation was repeated ten times for each classification. LASSO, fused_LASSO, and group LASSO are used in feature selection step for comparison. Results The accuracy and the selected features are the focus of clinical diagnosis reports, indicating that the feature selection algorithm is effective.