Magnetic resonance image selection for multi-atlas segmentation using mixture models

Abstract

In this paper, magnetic resonance image similarity metrics based on generative model induced spaces are introduced. Particularly, three generative-based similarities are proposed. Metrics are tested in an atlas selection task for multi-atlas-based image segmentation of basal ganglia structure, and compared with the mean square metric, as it is assessed on the high dimensional image domain. Attained results show that our proposal provides a suitable atlas selection and improves the segmentation of the structures of interest.