Segmentation of human facial muscles on ct and mri data using level set and bayesian methods

Kale, Hikmet Emre
Medical image segmentation is a challenging problem, and is studied widely. In this thesis, the main goal is to develop automatic segmentation techniques of human mimic muscles and to compare them with ground truth data in order to determine the method that provides best segmentation results. The segmentation methods are based on Bayesian with Markov Random Field (MRF) and Level Set (Active Contour) models. Proposed segmentation methods are multi step processes including preprocess, main muscle segmentation step and post process, and are applied on three types of data: Magnetic Resonance Imaging (MRI) data, Computerized Tomography (CT) data and unified data, in which case, information coming from both modalities are utilized. The methods are applied both in three dimensions (3D) and two dimensions (2D) data cases. A simulation data and two patient data are utilized for tests. The patient data results are compared statistically with ground truth data which was labeled by an expert radiologist.


Automatic Bayesian segmentation of human facial tissue using 3D MR-CT fusion by incorporating models of measurement blurring, noise and partial volume
Şener, Emre; Kanoğlu, Utku; Mumcuoğlu, Ünal Erkan; Department of Engineering Sciences (2012)
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. In this study, a new Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. The method incorporates a model to account for image blurring during data acquisition, a prior helping to reduce noise as well as a partial volume model. Regularization ba...
An Extensible security infrastructure for the secondary use of electronic health records in clinical research
Eryılmaz, Elif; Toroslu, İsmail Hakkı; Doğaç, Asuman; Department of Computer Engineering (2013)
In order to facilitate clinical research studies re-using Electronic Health Records (EHR) has a great potential. Besides interoperability, safeguarding the security and privacy of the medical data in the context of secondary use for clinical research is one of the most important challenges in this respect. In order to ensure that the clinical information is shared among EHR systems and clinical research systems in an ethical and safe way, there needs to be standards-based and adaptable security and privacy ...
Representation of human brain by mesh networks
Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
Automatic 3D segmentation of individual facial muscles using unlabeled prior information
Rezaeitabar, Yousef; Ulusoy, İlkay (Springer Science and Business Media LLC, 2012-01-01)
Purpose Segmentation of facial soft tissues is required for surgical planning and evaluation, but this is laborious using manual methods and has been difficult to achieve with digital segmentation methods. A new automatic 3D segmentation method for facial soft tissues in magnetic resonance imaging (MRI) images was designed, implemented, and tested.
A medical image processing and analysis framework
Çevik, Alper; Eyüboğlu, Behçet Murat; Oğuz, Kader Karlı; Department of Biomedical Engineering (2011)
Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis, treatment planning, and verification of administered treatment. Therefore, accuracy in analysis of medical images is at least as important as accuracy in data acquisition processes. Medical images require sequential application of several image post-processing techniques in order to be used for quantification and analysis of intended features. Main objective...
Citation Formats
H. E. Kale, “Segmentation of human facial muscles on ct and mri data using level set and bayesian methods,” M.S. - Master of Science, Middle East Technical University, 2011.