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Proceedings Paper

Identifying glaucoma with multi-fractal features from optical coherence tomography (OCT)
Author(s): P. Gunvant; P. Y. Kim; K. M. Iftekharuddin; E. A. Essock
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Paper Abstract

We propose a novel technique that exploits multi-fractal features for classifying glaucoma from ocular normal patients using retinal nerve fiber layer (RNFL) thickness measurement data. We apply a box-counting (BC) method, which utilizes pseudo 2D images from 1D RNFL data, and a multi-fractional Brownian motion (mBm) method, which incorporates both fractal and wavelet analyses, to analyze optical coherence tomography (OCT) data from 136 study participants (63 with glaucoma and 73 ocular normal patients). For statistical performance comparison, we compute the sensitivity, specificity and area under receiver operating curve (AUROC). The AUROCs in identifying glaucoma from ocular normal patients were 0.81 (BC), 0.87 (mBm), and 0.89 (BC+mBm), respectively.

Paper Details

Date Published: 16 March 2011
PDF: 9 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633S (16 March 2011); doi: 10.1117/12.877741
Show Author Affiliations
P. Gunvant, Western Univ. of Health Sciences (United States)
Southern College of Optometry (United States)
Univ. of Memphis (United States)
P. Y. Kim, The Univ. of Memphis (United States)
K. M. Iftekharuddin, Southern College of Optometry (United States)
The Univ. of Memphis (United States)
E. A. Essock, Univ. of Louisville (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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