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Permanent link (DOI): https://doi.org/10.7939/R3PG65

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Skeletonization and segmentation algorithms for object representation and analysis Open Access

Descriptions

Other title
Subject/Keyword
Skeletonization
Segmentation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Wang, Tao
Supervisor and department
Basu, Anup (Computing Science)
Cheng, Irene (Computing Science)
Examining committee member and department
Bischof, Walter (Computing Science)
Boulanger, Pierre (Computing Science)
Flores-Mir, Carlos (Dentistry)
Hamarneh, Ghassan (Computer Science, Simon Fraser University)
Department
Department of Computing Science
Specialization

Date accepted
2010-01-29T21:56:25Z
Graduation date
2010-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Skeletonization and segmentation are two important techniques for object representation and analysis. Skeletonization algorithm extracts the “centre-lines” of an object and uses them to efficiently represent the object. It has many applications in various areas, such as computer-aided design, computer-aided engineering, and virtual reality. Segmentation algorithm locates the target object or Region Of Interest (ROI) from images. It has been widely applied to medical image analysis and many other areas. This thesis presents two studies in skeletonization and two studies in segmentation that advanced the state-of-the-art research. The first skeletonization study suggests an improvement of an existing algorithm for connectivity preservation, which is one of the fundamental requirements for skeletonization algorithms. The second skeletonization study proposes a method to generate curve skeletons with unit-width, which is required by many applications. The first segmentation study presents a new approach named Flexible Vector Flow (FVF) to address a few problems of other active contour models such as insufficient capture range and poor convergence for concavities. This approach was applied to brain tumor segmentation in two dimensional (2D) space. The second segmentation study extends the 2D FVF algorithm to three-dimension (3D) and utilizes it to automatically segment brain tumors in 3D.
Language
English
DOI
doi:10.7939/R3PG65
Rights
License granted by Tao Wang (tw@ualberta.ca) on 2010-01-29T18:31:15Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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