Dissertation/Thesis Abstract

Automated three-dimensional segmentation of intraretinal layers from optical coherence tomography images centered on the optic nerve head
by Antony, Bhavna Josephine, M.S., The University of Iowa, 2009, 67; 1473720
Abstract (Summary)

Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of retinal diseases. These high-resolution images of the retina allow structural changes to be detected and tracked. For instance, in glaucoma, the retinal nerve fiber layer (RNFL) has been known to thin. The recent availability of the considerably larger volumetric data from the spectral-domain OCT scanners has further increased the need for new processing techniques. This body of work is centered around an automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected through the computation of a minimum-cost closed set in a vertex-weighted graph constructed using edge/regional information, and subject to a priori determined varying surface interaction and smoothness constraints. The method also addresses the challenges posed by presence of the neural canal and the large blood vessels found at the ONH. The method was used to study RNFL thickness maps of normal and glaucomatous eyes, which showed average thicknesses of 73.72 +/- 32.72um and 60.38 +/- 25.22um (p < 0.01), respectively.

Indexing (document details)
Advisor: Garvin, Mona K.
Commitee: Abramoff, Michael, Sonka, Milan
School: The University of Iowa
Department: Electrical and Computer Engineering
School Location: United States -- Iowa
Source: MAI 48/03M, Masters Abstracts International
Source Type: DISSERTATION
Subjects: Biomedical engineering, Electrical engineering
Keywords: Automated 3-D segmentation, Glaucoma, Graph search, Optical coherence tomography, Retinal surfaces
Publication Number: 1473720
ISBN: 9781109584110
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