Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation Academic Article uri icon


MeSH Major

  • Brain
  • Image Processing, Computer-Assisted
  • Machine Learning
  • Magnetic Resonance Imaging


  • 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 require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

publication date

  • September 2015



  • Academic Article



  • eng

PubMed Central ID

  • PMC4562064

Digital Object Identifier (DOI)

  • 10.1109/JBHI.2015.2439242

PubMed ID

  • 26340685

Additional Document Info

start page

  • 1598

end page

  • 609


  • 19


  • 5