Structural classification of glaucomatous optic neuropathy

Structural classification of glaucomatous optic neuropathy

Bibliographic Details
Main Author: Twa, Michael Duane
Language:English
Published: The Ohio State University / OhioLINK 2006
Subjects:
Glaucoma
Decision Trees
Medical Imaging
Machine Learning
Zernike polynomials
Wavelets
B-Splines
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1155267844
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu11552678442021-08-03T05:51:19Z Structural classification of glaucomatous optic neuropathy Twa, Michael Duane Glaucoma Decision Trees Medical Imaging Machine Learning Zernike polynomials Wavelets B-Splines Glaucoma is a leading cause of blindness. Quantitative methods of imaging the optic nerve head (e.g. confocal scanning laser tomography) are increasingly used to diagnose glaucomatous optic neuropathy and monitor its progression, yet there is considerable controversy about how to interpret and make best use of this structural information. In this research, machine learning methods are proposed and evaluated as alternatives to current methods of disease classification. First, multiple mathematical modeling methods such as radial polynomials, wavelet analysis and B-spline fitting were used to reconstruct topographic descriptions of the optic nerve head and peripapillary region. Next, features derived from these models were extracted and used as classification features for automated decision tree induction. Decision tree classification performance was compared with conventional techniques such as expert grading of stereographic photos, Moorfields Regression Analysis, and visual field-based standards for the cross-sectional identification of glaucomatous optic neuropathy. Pseudozernike polynomial modeling methods provided the most compact and faithful representation of these structural data, albeit at considerably greater computational expense when compared to wavelet and B-spline modeling methods. The pseudozernike-based classifier had the greatest area under the receiver-operating characteristic (ROC) curve, 85% compared to 73% and 71% for the wavelet and B-spline-based classification models respectively. These results show that automated analysis of optic nerve head structural features can identify glaucomatous optic neuropathy in very good agreement with expert assessments of stereographic disc photos. Moreover, these quantitative methods can improve the standardization and agreement of these assessments. Extensions of these methods may provide alternative ways to evaluate structural and functional disease relationships in glaucoma. 2006-09-13 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1155267844 http://rave.ohiolink.edu/etdc/view?acc_num=osu1155267844 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Glaucoma
Decision Trees
Medical Imaging
Machine Learning
Zernike polynomials
Wavelets
B-Splines
spellingShingle Glaucoma
Decision Trees
Medical Imaging
Machine Learning
Zernike polynomials
Wavelets
B-Splines
Twa, Michael Duane
Structural classification of glaucomatous optic neuropathy
author Twa, Michael Duane
author_facet Twa, Michael Duane
author_sort Twa, Michael Duane
title Structural classification of glaucomatous optic neuropathy
title_short Structural classification of glaucomatous optic neuropathy
title_full Structural classification of glaucomatous optic neuropathy
title_fullStr Structural classification of glaucomatous optic neuropathy
title_full_unstemmed Structural classification of glaucomatous optic neuropathy
title_sort structural classification of glaucomatous optic neuropathy
publisher The Ohio State University / OhioLINK
publishDate 2006
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1155267844
work_keys_str_mv AT twamichaelduane structuralclassificationofglaucomatousopticneuropathy
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