Segmentation of thalamus from MR images via task-driven dictionary learning

Luoluo Liu, Jeffrey Glaister, Xiaoxia Sun, Aaron Carass, Trac D. Tran, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is pro- posed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation overstate-of-the-art atlas-based thalamus segmentation algorithms.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationImage Processing
EditorsMartin A. Styner, Elsa D. Angelini, Elsa D. Angelini
ISBN (Electronic)9781510600195
StatePublished - 2016
EventMedical Imaging 2016: Image Processing - San Diego, United States
Duration: Mar 1 2016Mar 3 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2016: Image Processing
Country/TerritoryUnited States
CitySan Diego


  • Dictionary learning and sparse representation
  • Diffiusion tensor imaging
  • Segmentation
  • Thalamus

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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