Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling. Issue 124 (February 2016)

• Record Type: Journal Article
• Authors: Şener, Emre; Mumcuoglu, Erkan U.; Hamcan, Salih
• Is Part Of: Computer methods and programs in biomedicine. Issue 124(2016)
• Page Start: 31
• Page End: 44
• Publication Date: 2016-02
• Subjects: Image segmentation -- Information fusion -- Partial volume -- Resolution enhancement -- Superresolution -- Human facial tissue; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.10.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Bayesian method provides resolution enhanced segmentation of human head. Segmentation classes incllude muscle, bone, fat, air and skin. Tests were performed on 3D MR and CT images, as well as registered MR-CT images. The most successful results were obtained by the information fusion of MR and CT. Free parameters of the algorithm can be adjusted in a more systematic way. 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. Objectives: 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.