title: Automatic segmentation of human facial tissue by MRI-CT fusion: a feasibility study.
abstract: The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), computerized tomography (CT) and fusion, in which case information from both modalities were utilized maximally for every tissue type. The results on human data indicated that fusion, thickness adaptive and postprocessing options provided the best muscle/fat segmentation scores in both Level Set and Bayesian methods. When the best Level Set and Bayesian methods were compared, scores of the latter were better. Number of algorithm parameters (to be trained) and computer run time measured were also in favour of the Bayesian method.
authors.affiliation: Health Informatics Department, Informatics Institute, Middle East Technical University, Ankara, Turkey. emrevekale@yahoo.com
authors.forename: Emre H
authors.initials: EH
authors.lastname: Kale
authors.forename: Erkan U
authors.initials: EU
authors.lastname: Mumcuoglu
authors.forename: Salih
authors.initials: S
authors.lastname: Hamcan
mesh_terms: D001331:Automation; D001499:Bayes Theorem; D005145:Face; D005240:Feasibility Studies; D006801:Humans; D008279:Magnetic Resonance Imaging; D011474:Prosthesis Design; D014057:Tomography, X-Ray Computed