Please use this identifier to cite or link to this item:
Title: Modeling of the human upper airway from multimodal 3D dentofacial images
Keywords: image segmentation, upper airway, cone-beam CT, magnetic resonance images,nasal cavity and paranasal sinuses, Kullback-Lebler divergence
Issue Date: 30-Sep-2014
Citation: BUI NHAT LINH (2014-09-30). Modeling of the human upper airway from multimodal 3D dentofacial images. ScholarBank@NUS Repository.
Abstract: A patient-specific virtual upper airway model is important for clinical, education, and research applications. Cone beam computed tomography (CBCT) and magnetic resonance (MR) are popular imaging modalities of the upper airway but automatic segmentation of these images is still challenging due to noise and the complex anatomical features, especially at the region of the nasal cavity and paranasal sinuses. In this thesis, we describe segmentation schemes for the segmentation of nasal cavity and paranasal sinuses from CBCT images and the nasal passage from MR images. First, we present a multi-step level set segmentation scheme based on Kullback-Leibler divergence and anisotropic localized active contour for the automatic segmentation of CBCT images to create a 3D model of the nasal cavity and paranasal sinuses. Second, we propose a graph-based method for the segmentation of thin volumetric structures and apply it to segment the nasal passage from head MR images. Third, we register the 3D surface mesh of the dental study model to the head MR images to create the entire upper airway model using only non-radiation imaging modalities.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
BuiNL.pdf11.32 MBAdobe PDF



Page view(s)

checked on Nov 9, 2018


checked on Nov 9, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.