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.


Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging
Çiftçioğlu, Mustafa Ulaş; Gökçay, Didem; Department of Medical Informatics (2011)
Segmentation of sub-cortical Gray Matter (GM) structures in magnetic resonance brain images is crucial in clinic and research for many purposes such as early diagnosis of neurological diseases, guidance of surgical operations and longitudinal volumetric studies. Unfortunately, the algorithms that segment the brain into 3 tissues usually suffer from poor performance in the sub-cortical region. In order to increase the detection of sub-cortical GM structures, an evolutionary hierarchical region merging approa...
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...
Expectation based evaluation framework for hospital information systems measurement model )
Gürsel, Güney; Saka, Osman; Arifoğlu, Ali; Department of Health Informatics (2012)
Evaluation is essential for Medical Informatics as well as many other disciplines. There is a growing interest and investment for evaluation researches and self evaluation works. Hospital Information System (HIS) evaluation frameworks have largely been discussed in the literature. However, existing frameworks lack one important aspect, to what extent user expectations from HIS are met. To complement this deficiency we designed an evaluation farmework for evaluating the user expectation in HIS. User expectat...
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.
Segmentation registration and visualization of medical images for treatment planning
Tuncer, Özgür; Severcan, Mete; Department of Electrical and Electronics Engineering (2003)
Medical imaging has become the key to access inside human body for the purpose of diagnosis and treatment planning. In order to understand the effectiveness of planned treatment following the diagnosis, treated body part may have to be monitored several times during a period of time. Information gained from successive imaging of body part provides guidance to next step of treatment. Comparison of images or datasets taken at different times requires registration of these images or datasets since the same con...
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.