16 March 2011 Identifying glaucoma with multi-fractal features from optical coherence tomography (OCT)
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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.
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P. Gunvant, P. Gunvant, P. Y. Kim, P. Y. Kim, K. M. Iftekharuddin, K. M. Iftekharuddin, E. A. Essock, E. A. Essock, } "Identifying glaucoma with multi-fractal features from optical coherence tomography (OCT)", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633S (16 March 2011); doi: 10.1117/12.877741; https://doi.org/10.1117/12.877741
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