Classification of Alzheimer’s Disease Based on Multiple Anatomical Structures’ Asymmetric Magnetic Resonance Imaging Feature Selection

Abstract

The quality of magnetic resonance imaging (MRI) features is key to the classification of Alzheimer’s disease. However, relevant research has as yet paid little attention to asymmetric MR features. In this paper, the asymmetric MR features of multiple anatomical structures are extracted. The MR feature types include volume feature and several kinds of texture features. Subsequently, the extracted features are selected based on the wrapper feature selection method with chain-like agent genetic algorithm (CAGA) and support vector machine (SVM). Finally, the selected asymmetric MR features are used for classification of Alzheimer’s disease. Experimental results show that the extracted features have apparent asymmetrical characteristics. The asymmetric volume feature of single anatomical structure can have better discrimination capability than the whole volume feature of same anatomical structure. Single selected asymmetric MR feature has displayed a superior discrimination capability in regards to three conditions of Alzheimer’s disease. The improvement is very apparent compared to before feature selection and the p-value-based feature selection method. In conclusion, this proposed method offer a new kind of feature type and can improve the classification rate for diagnosis of Alzheimer’s disease.