Cart (Loading....) | Create Account
Close category search window
 

Partial volume segmentation of medical images

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)
Xiang Li ; Dept. of Radiol., State Univ. of New York, Stony Brook, NY, USA ; Eremina, D. ; Lihong Li ; Zhengrong Liang

Image segmentation plays an important role in medical image processing. The aim of conventional hard segmentation methods is to assign a unique label to each voxel. However, due to the limited spatial resolution of medical imaging equipment and the complex anatomic structure of soft tissues, a single voxel in a medical image may be composed of several tissue types, which is called partial volume (PV) effect. Using the hard segmentation methods, the PV effect can substantially decrease the accuracy of quantitative measurements and the quality of visualizing different tissues. In this paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing an image segmentation framework of maximum a posterior (MAP) probability. A new Markov random field (MRF) model was used to reflect the spatial information for the tissue mixture. Parameters of each tissue class were estimated through the expectation-maximization (EM) algorithm during the MAP tissue mixture segmentation. The MAP-EM mixture segmentation methodology was tested by digital phantom MR and patient CT images with PV effect evaluation. Results demonstrated that a hard segmentation method would lose a significant amount of details along the tissue boundaries, while the presented new PV segmentation method can dramatically improve the performance of preserving the details.

Published in:

Nuclear Science Symposium Conference Record, 2003 IEEE  (Volume:5 )

Date of Conference:

19-25 Oct. 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.