Classifying the Course of Alzheimer's Disease with Brain MR Images and a Method Based on Three-Dimensional Local Pattern Transformation

Abstract

A classification method was developed to differentiate cognitive normal controls (CN), mild cognitive impairment (MCI) patients and Alzheimer's disease (AD) patients from the magnetic resonance (MR) image data. Three-dimensional (3D) local pattern transformation was used in the proposed method to obtain texture features, which were then fused with the conventional image features for the classification purposes. Region of interests (i.e., bilateral hippocampus, gray matter and white matter) were selected from the MR images of 46 CN, 61 MCI patients and 25 AD patients, from which the 3D local pattern transformation texture features and conventional image features were extracted, fused and used for classification with the support vector machine. It was demonstrated that the accuracy, sensitivity, specificity and area under the curve (AUC) were 88.73%, 78.00%, 95.7% and 0.886 5, respectively, for the fused texture feature/conventional image features in bilateral hippocampus of CN controls and AD patients. In comparison, the fused features in the gray matter gave an accuracy, sensitivity, specificity and AUC of 85.92%, 80.00%, 86.6% and 0.854 3, respectively. It is concluded that the texture features extracted from 3D local pattern transform in hippocampus could be used in conjunction with the conventional image features for better classification of the course of Alzheimer's disease.

Keywords

• brain magnetic resonance image
• texture feature
• three-dimensional local pattern transform
• Alzheimer's disease
• classification