Metric space structures for computational anatomy

Abstract

This paper describes a method based on metric structures for anatomical analysis on a large set of brain MR images. A geodesic distance between each pair was measured using large deformation diffeomorphic metric mapping (LDDMM). Manifold learning approaches were applied to seek a low-dimensional embedding in the high- dimensional shape space, in which inference between healthy control and disease groups can be done using standard classification algorithms. In particular, the proposed method was evaluated on ADNI, a dataset for Alzheimer's disease study. Our work demonstrates that the high-dimensional anatomical shape space of the amygdala and hippocampi can be approximated by a relatively low dimension manifold. © 2013 Springer International Publishing.