By Topic

Automated MAP-MRF EM labelling for volume determination in PET

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

6 Author(s)
Gribben, H. ; Inst. of Electron., Queen''s Univ. Belfast, Belfast ; Miller, P. ; Hongbin Wang ; Carson, K.
more authors

An automated, unsupervised Maximum a Posterior - Markov Random Field Expectation Maximisation (MAP- MRF EM) Labelling technique, based upon a Bayesian framework, for volume of interest (VOI) determination in Positron Emission Tomography (PET) imagery is proposed. The segmentation technique incorporates MAP-MRF modelling into a mixture modelling approach using the EM algorithm, to consider both the structural and statistical nature of the data. The performance of the algorithm has been assessed on a set of PET phantom data. Investigations revealed improvements over a simple statistical approach using the EM algorithm, and improvements over a MAP- MRF approach, using the output from the EM algorithm as an initial estimate. Improvement is also shown over a standard semi-automated thresholding method, and an automated Fuzzy Hidden Markov Chain (FHMC) approach; particularly for smaller object volume determination, as the FHMC method loses some spatial correlation. A deblurring pre-processing stage was also found to provide improved results.

Published in:

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Date of Conference:

14-17 May 2008