Date of Award

Winter 2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Modeling Simul & Visual Engineering

Committee Director

Michel A. Audette

Committee Member

Frederic D. McKenzie

Committee Member

Jiang Li

Committee Member

Stacie I. Ringleb

Committee Member

Jerome Schmid

Abstract

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation.

DOI

10.25777/93jp-zw72

ISBN

9781339868165

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