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

Download

index.pdf

Date

2011

Author

Kale, Hikmet Emre

Metadata

Show full item record

Item Usage Stats

134
views

60
downloads

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.

Subject Keywords

Medical Informatics., Imaging systems in medicine.

URI

http://etd.lib.metu.edu.tr/upload/12613352/index.pdf
https://hdl.handle.net/11511/20599

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

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...
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 ...
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...

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.