The Effect of Labeled/Unlabeled Prior Information for Masseter Segmentation

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.