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.