Please use this identifier to cite or link to this item:
Title: Medical image analysis using statistical shape model based on subdivision surface wavelet
Authors: LI YANG
Keywords: model-guided segmentation, subdivision surface wavelet, statistical shape model, scale-space
Issue Date: 2-Jun-2008
Citation: LI YANG (2008-06-02). Medical image analysis using statistical shape model based on subdivision surface wavelet. ScholarBank@NUS Repository.
Abstract: In this thesis, we propose a novel statistical shape model based on the shape representation using subdivision surface wavelets. It has three highly desirable properties of a statistical shape model: compact shape representation, multi-scale shape description and spatial-localization of the shape variation. We also develop a new model-guided segmentation framework utilizing this Statistical Surface Wavelet Model (SSWM) as a shape prior, in which the segmentation task is formulated as an optimization problem to best fit the statistical shape model with an input image. Due to the localization property of the wavelet shape representation both in scale and space, this multi-dimensional optimization problem can be efficiently solved in a multi-scale and spatially localized manner. We have applied our method to segment caudate nucleus and putamen from MRI. The results show that our segmentation method is robust, computationally efficient and achieves a high degree of segmentation accuracy.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
LiYangThesisFinal.pdf20.1 MBAdobe PDF



Page view(s)

checked on Apr 19, 2019


checked on Apr 19, 2019

Google ScholarTM


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