Groupwise shape correspondence with local features

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Last Modified
  • March 20, 2019
Creator
  • Oguz, Ipek
    • Affiliation: College of Arts and Sciences, Department of Computer Science
Abstract
  • 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.
Date of publication
DOI
Resource type
Rights statement
  • In Copyright
Advisor
  • Styner, Martin
Language
Access right
  • Open access
Date uploaded
  • October 11, 2010

Relations

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details Groupwise shape correspondence with local features 2019-04-11 Public Download