About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 928469, 12 pages
http://dx.doi.org/10.1155/2013/928469
Research Article

The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation

1Biomedical Engineering Department, Middle East Technical University, 06800 Ankara, Turkey
2Electrical Engineering Department, Middle East Technical University, 06800 Ankara, Turkey

Received 7 March 2013; Accepted 5 June 2013

Academic Editor: Marco Perez-Cisneros

Copyright © 2013 Yousef Rezaei Tabar and Ilkay Ulusoy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Several segmentation methods are implemented and applied to segment the facial masseter tissue from magnetic resonance images. The common idea for all methods is to take advantage of prior information from different MR images belonging to different individuals in segmentation of a test MR image. Standard atlas-based segmentation methods and probabilistic segmentation methods based on Markov random field use labeled prior information. In this study, a new approach is also proposed where unlabeled prior information from a set of MR images is used to segment masseter tissue in a probabilistic framework. The proposed method uses only a seed point that indicates the target tissue and performs automatic segmentation for the selected tissue without using labeled training set. The segmentation results of all methods are validated and compared where the influences of labeled or unlabeled prior information and initialization are discussed particularly. It is shown that if appropriate modeling is done, there is no need for labeled prior information. The best accuracy is obtained by the proposed approach where unlabeled prior information is used.