Dissertation/Thesis Abstract

Groupwise shape correspondence with local features
by Oguz, Ipek, Ph.D., The University of North Carolina at Chapel Hill, 2009, 122; 3387964
Abstract (Summary)

Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. However, anatomical correspondence is rarely a direct result of spatial proximity of sample points but rather depends on many other features such as local curvature, position with respect to blood vessels, or connectivity to other parts of the anatomy.

This dissertation presents a novel method for computing point-based correspondence among populations of surfaces by combining spatial location of the sample points with non-spatial local features. A framework for optimizing correspondence using arbitrary local features is developed. The performance of the correspondence algorithm is objectively assessed using a set of evaluation metrics.

The main focus of this research is on correspondence across human cortical surfaces. Statistical analysis of cortical thickness, which is key to many neurological research problems, is the driving problem. I show that incorporating geometric (sulcal depth) and non-geometric (DTI connectivity) knowledge about the cortex significantly improves cortical correspondence compared to existing techniques. Furthermore, I present a framework that is the first to allow the white matter fiber connectivity to be used for improving cortical correspondence.

Indexing (document details)
Advisor: Styner, Martin A.
Commitee: Marron, J. Stephen, Niethammer, Marc, Pizer, Stephen M., Whitaker, Ross T.
School: The University of North Carolina at Chapel Hill
Department: Computer Science
School Location: United States -- North Carolina
Source: DAI-B 71/01, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Biomedical engineering, Medical imaging, Computer science
Keywords: Correspondence, Local features, Medical imaging, Neuroimaging, Shape correspondence, Statistical shape analysis
Publication Number: 3387964
ISBN: 9781109546781
Copyright © 2018 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest