By Topic

Automatic MRI brain tissue segmentation using a hybrid statistical and geometric model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Huang, A. ; Dept. of Elec. & Comp. Eng., British Columbia Univ., Vancouver, BC ; Abugharbieh, R. ; Tam, R. ; Traboulsee, A.

This paper presents a novel hybrid segmentation technique incorporating a statistical as well as a geometric model in a unified segmentation scheme for brain tissue segmentation of magnetic resonance imaging (MRI) scans. We combine both voxel probability and image gradient and curvature information for segmenting gray matter (GM) and white matter (WM) tissues. Both qualitative and quantitative results on synthetic and real brain MRI scans indicate superior and consistent performance when compared with standard techniques such as SPM and FAST

Published in:

Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on

Date of Conference:

6-9 April 2006