Quantitative measurements from segmentations of human brain magnetic resonance (MR)
images provide important biomarkers for normal aging and disease progression. In this
paper, we propose a patch-based tissue classification method from MR images that uses a
sparse dictionary learning approach and atlas priors. Training data for the method consists
of an atlas MR image, prior information maps depicting where different tissues are expected
to be located, and a hard segmentation. Unlike most atlas-based classification methods that …

Quantitative measurements from segmentations of soft tissues from magnetic resonance
images (MRI) of human brains provide important biomarkers for normal aging, as well as
disease progression. In this paper, we propose a patch-based tissue classification method
from MR images using sparse dictionary learning from an atlas. Unlike most atlas-based
classification methods, deformable registration from the atlas to the subject is not required.
An “atlas” consists of an MR image, its tissue probabilities, and the hard segmentation. The …