Classification of Alzheimer's disease based on the combination of morphometric feature and texture feature

Abstract

The identification of discriminative features of the Alzheimer's disease contributes to the diagnostic accuracy. Recently, the combination of different types of features has been actively used in the area of the AD classification. In this paper, we proposed a novel classification framework to jointly select features, which are extracted from the VBM analysis and texture analysis to distinguish between the AD and the NC. Furthermore, in order to capture robust discriminative features, we improve the feature subset selection by combining the SVM-RFE and covariance to take into account the relationship among features. In order to evaluate the proposed method, we have performed evaluations on the MRI acquiring from the ADNI database. Our experimental results showed the feature combination has better performance than the either morphometric features and texture features. Also, we demonstrated our method is better than the one without feature selection, PCA or others.