title: Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling.
abstract: BACKGROUND
  Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification.
  
  
  OBJECTIVE
  In this study, a Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin.
  
  
  METHODS
  The method presented incorporates information fusion from multiple modalities, modelling of image resolution (measurement blurring), image noise, two priors helping to reduce noise and partial volume. Image resolution modelling employed facilitates resolution enhancement and superresolution capabilities during image segmentation. Regularization based on isotropic and directional Markov Random Field priors is integrated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of the image. Sub-methods as variations of the main method are generated by using a combination of the models.
  
  
  RESULTS
  Testing of the sub-methods is performed on two patients using single modality three-dimensional (3D) image (magnetic resonance, MR or computerized tomography, CT) as well as registered MR-CT images with information fusion. Numerical, visual and statistical analyses of the methods are conducted. High segmentation accuracy values are obtained by the use of image resolution and partial volume models as well as information fusion from MR and CT images. The methods are also compared with our Bayesian segmentation method proposed in a previous study. The performance is found to be similar to our previous Bayesian approach, but the presented methods here eliminates ad hoc parameter tuning needed by the previous approach which is system and data acquisition setting dependent.
  
  
  CONCLUSIONS
  The Bayesian approach presented provides resolution enhanced segmentation of very thin structures of the human head. Meanwhile, free parameters of the algorithm can be adjusted for different imaging systems and data acquisition settings in a more systematic way as compared with our previous study.
authors.affiliation: Department of Engineering Sciences, Middle East Technical University, Ankara, Turkey. Electronic address: sener_emre@yahoo.com.
authors.forename: Emre
authors.initials: E
authors.lastname: Şener
authors.affiliation: Health Informatics Department, Informatics Institute, Middle East Technical University, Ankara, Turkey. Electronic address: merkan@metu.edu.tr.
authors.forename: Erkan U
authors.initials: EU
authors.lastname: Mumcuoglu
authors.affiliation: Department of Radiology, Gulhane Military Medical School, Ankara, Turkey. Electronic address: slhhmcn@hotmail.com.
authors.forename: Salih
authors.initials: S
authors.lastname: Hamcan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Human facial tissue; Image segmentation; Information fusion; Partial volume; Resolution enhancement; Superresolution