干旱区生态环境知识资源中心

This paper presents a fully automatic method that condenses relevant morphometric information from a database of magnetic resonance images (MR) labeled as either normal (NC) or Alzheimer's disease (AD). The proposed method generates class templates using Nonnegative Matrix Factorization (NMF) which will be used to develop an NC/AD classifier. It then finds regions of interest (ROI) with discerning inter-class properties. by inspecting the difference volume of the two class templates. From these templates local probability distribution functions associated to low level features such as intensities, orientation and edges within the found ROI are calculated. A sample brain volume can then be characterized by a similarity measure in the ROI to both the normal and the pathological templates. These characteristics feed a simple binary SVM classifier which, when tested with an experimental group extracted from a public brain MR dataset (OASIS), reveals an equal error rate (EER) measure which is better than the state-of-the-art tested on the same dataset (0.1 in the former and 0.2 in the latter).