Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/107437
Title: DENSE CORRESPONDENCE AND STATISTICAL SHAPE RECONSTRUCTION OF FRACTURED, INCOMPLETE SKULLS
Authors: ZHANG KUN
Keywords: dense correspondence, skull reconstruction, reconstruction error estimation, anatomical consistency, close matching, statistical shape model
Issue Date: 21-Jul-2014
Source: ZHANG KUN (2014-07-21). DENSE CORRESPONDENCE AND STATISTICAL SHAPE RECONSTRUCTION OF FRACTURED, INCOMPLETE SKULLS. ScholarBank@NUS Repository.
Abstract: Human skulls can be fractured or incomplete due to impact fracture or congenital defects. In this thesis, a statistical shape model for skulls is built to reconstruct the normal appearance of fractured, incomplete skulls. To build the statistical shape model, a dense correspondence algorithm is proposed to resample the skulls. With anatomical landmarks as hard constraints to ensure anatomically consistency, and control points sampled on the skull surfaces as soft constraints to provide additional local shape constraints to ensure close matching, the proposed dense correspondence algorithm is more accurate than existing methods. The error source of skull reconstruction is also investigated. The two primary sources of skull reconstruction error are resampling error and ASM fitting error. Test results also show that there is a linear relationship between ASM fitting error and reconstruction error, which allows reconstruction error of real fractured, incomplete skulls to be estimated by ASM fitting error.
URI: http://scholarbank.nus.edu.sg/handle/10635/107437
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ZhangK01.pdf20.63 MBAdobe PDF

OPEN

NoneView/Download
ZhangK02.pdf19.33 MBAdobe PDF

OPEN

NoneView/Download
ZhangK03.pdf17.76 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

60
checked on Dec 11, 2017

Download(s)

28
checked on Dec 11, 2017

Google ScholarTM

Check


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