Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16309
Title: Automated segmentation of soft tissue in abdominal CT scans
Authors: LIM SHER EE DENNIS
Keywords: Medical Image Segmentation CT IDD Snakes
Issue Date: 6-Aug-2009
Source: LIM SHER EE DENNIS (2009-08-06). Automated segmentation of soft tissue in abdominal CT scans. ScholarBank@NUS Repository.
Abstract: 3D models are necessary for many medical procedures. Current techniques require manual segmentation which is tedious and results in coarse models. A current challenge would be to develop automated or semi-automated segmentation techniques to replace this manual segmentation.Atlas-based algorithms are commonly used in medical image segmentatio. Atlases increase the robustness of the algorithm, producing more accurate results. This thesis proposes an automated, non-probabilistic segmentation algorithm for the segmentation of the abdomen from CT slices. The algorithm is designed as a multi-stage pipeline. After pre-processing, the algorithm registers the contours of the atlas to the CT image via a global registration stage and two local registration stages. This thesis also introduces a hybrid active contour called Iterative Corresponding Snake which exhibits greater robustness than the original and is more successful in converging to correct edges in the image.The algorithm was tested for convergence, accuracy and robustness with good results.
URI: http://scholarbank.nus.edu.sg/handle/10635/16309
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
LimSED.pdf3.82 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

249
checked on Jan 15, 2018

Download(s)

208
checked on Jan 15, 2018

Google ScholarTM

Check


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