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

doi:10.1117/12.2214206

21 March 2016 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

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97843H (2016) https://doi.org/10.1117/12.2214206
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

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.

Citation Download Citation

Luoluo Liu, Jeffrey Glaister, Xiaoxia Sun, Aaron Carass, Trac D. Tran, and Jerry L. Prince "Segmentation of thalamus from MR images via task-driven dictionary learning", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97843H (21 March 2016); https://doi.org/10.1117/12.2214206

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available