Automatic Bayesian segmentation of human facial tissue using 3D MR-CT fusion by incorporating models of measurement blurring, noise and partial volume

Download

index.pdf

Date

2012

Author

Şener, Emre

Metadata

Show full item record

Item Usage Stats

213
views

64
downloads

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 based on isotropic and directional Markov Random Field priors are integrated to the algorithm and their effects on segmentation accuracy are investigated. The Bayesian model is solved iteratively yielding tissue class labels at every voxel of an image. Sub-methods as variations of the main method are generated by switching on/off a combination of the models. Testing of the sub-methods are performed on two patients using single modality three-dimensional (3D) images as well as registered multi-modal 3D images (Magnetic Resonance and Computerized Tomography). Numerical, visual and statistical analyses of the methods are conducted. Improved segmentation accuracy is obtained through the use of the proposed image models and multi-modal data. The methods are also compared with the Level Set method and an adaptive Bayesiansegmentation method proposed in a previous study.

Subject Keywords

Imaging systems in medicine., Three-dimensional imaging in medicine., Human face recognition (Computer science)., Bayesian field theory., Face.

URI

http://etd.lib.metu.edu.tr/upload/12615091/index.pdf
https://hdl.handle.net/11511/22116

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling Şener, Emre; Mumcuoğlu, Ünal Erkan; Hamcan, Salih (2016-02-01) 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.
Segmentation of human facial muscles on ct and mri data using level set and bayesian methods Kale, Hikmet Emre; Mumcuoğlu, Ünal Erkan; Department of Medical Informatics (2011) 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...
Automatic segmentation of human facial tissue by MRI-CT fusion: A feasibility study Kale, Emre H.; Mumcuoğlu, Ünal Erkan; HAMCAN, Salih (2012-12-01) The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), compu...
Optimal design of sparse mimo arrays for wideband near-field imaging based on a statistical framework Kocamış, Mehmet Burak; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2018) Wideband near-field imaging is an emerging remote sensing technique in various applications such as airport security, surveillance, medical diagnosis, and through-wall imaging. Recently, there has been increasing interest in using sparse multiple-input-multiple-output (MIMO) arrays to achieve high resolution with reduced hardware complexity and cost. In this thesis, based on a statistical framework, an optimal design method is presented for two-dimensional MIMO arrays in wideband near-field imaging. Differe...
Image segmentation with Improved region modeling Ersoy, Ozan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2004) Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) ...

Citation Formats

E. Şener, “Automatic Bayesian segmentation of human facial tissue using 3D MR-CT fusion by incorporating models of measurement blurring, noise and partial volume,” Ph.D. - Doctoral Program, Middle East Technical University, 2012.