Classification for Alzheimer´s disease based on SVM using a spatial texture feature of cortical thickness

Features defined on the cortical surface derived from magnetic resonance imaging provide important information to diagnosis the Alzheimer´s disease (AD) and its premonitory symptoms Mild Cognitive Impairment (MCI). In general, the methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. In this paper, we propose an innovative method to describe the spatial texture information of cortical thickness named Spatial Multi-scale Block Local Binary Patterns. And combine this information with the region-wise characters of cortical thickness as the final classification features. The performance based on the proposed method is significantly higher for the AD/NC, AD/MCI and sMCI/cMCI groups than using PCA or taking the average of cortical thickness zones as classification features.