This study aims to explore the diagnosis in patients with Alzheimer's disease (AD) based on magnetic resonance (MR) images, and to compare the differences of bilateral hippocampus in classification and recognition. MR images were obtained from 25 AD patients and 25 normal controls (NC) respectively. Three-dimensional texture features were extracted from bilateral hippocampus of each subject. The texture features that existed significant differences between AD and NC were used as the features in a classification procedure. Back propagation (BP) neural network model was built to classify AD patients from healthy controls. The classification accuracy of three methods, which were principal components analysis, linear discriminant analysis and non-linear discriminant analysis, was obtained and compared. The correlations between bilateral hippocampal texture parameters and Mini-Mental State Examination (MMSE) scores were calculated. The classification accuracy of nonlinear discriminant analysis with a neural network model was the highest, and the classification accuracy of right hippocampus was higher than that of the left. The bilateral hippocampal texture features were correlated to MMSE scores, and the relative of right hippocampus was higher than that of the left. The neural network model with three-dimensional texture features could recognize AD patients and NC, and right hippocampus might be more helpful to AD diagnosis.
Citation： YULu, XIAHong, LIUWeifang. Classification Studies in Patients with Alzheimer's Disease and Normal Control Group Based on Three-dimensional Texture Features of Hippocampus Magnetic Resonance Images. Journal of Biomedical Engineering, 2016, 33(6): 1090-1094. doi: 10.7507/1001-5515.20160174 Copy