An Automatic Unsupervised Classification of MR Images in Alzheimer's Disease

Abstract

Image-analysis methods play an important role in helping detect brain changes in and diagnosis of Alzheimer's Disease (AD). In this paper, we propose an automatic unsupervised classification approach to distinguish brain magnetic resonance (MR) images of AD patients from those of elderly normal controls. The symmetric log-domain diffeomorphic demons algorithm, with the properties of symmetry and invertibility, is used to compute the pair-wise registration, whose deformation field is then used to calculate the Riemannian distance between them. The spectral embedding algorithm is performed based on the Riemannian distance matrix to project images onto a low-dimensional space where each image is represented as a point and its neighboring points correspond to images of high anatomical similarity. Finally, the quick shift clustering method is employed in the embedded space to partition the dataset into subgroups. The experiments using the proposed method show very good performance for clustering images into AD and normal aging, using the Clinical Dementia Rating (CDR) scale as a comparison.