Novel Fractal Feature-Based Multiclass Glaucoma Detection and Progression Prediction

We investigate the use of fractal analysis (FA) as the basis of a system for multiclass prediction of the progression of glaucoma. FA is applied to pseudo two-dimensional images converted from one-dimensional retinal nerve fiber layer (RNFL) data obtained from the eyes of normal subjects, and from subjects with progressive and non-progressive glaucoma. FA features are obtained using a box-counting method and a multi-fractional Brownian motion method that incorporates texture and multiresolution analyses. Both features are used for Gaussian kernel-based multiclass classification. Sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) are computed for the FA features and for metrics obtained using wavelet-Fourier analysis (WFA) and fast-Fourier analysis (FFA). The AUROCs that predict progressors from non-progressors based on classifiers trained using a dataset comprised of non-progressors and ocular normal subjects are 0.70, 0.71 and 0.82 for WFA, FFA, and FA, respectively. The correct multiclass classification rates among progressors, non-progressors, and ocular normal subjects are 0.82, 0.86 and 0.88 for WFA, FFA, and FA, respectively. Simultaneous multiclass classification among progressors, non-progressors, and ocular normal subjects has not been previously described. The novel FA-based features achieve better performance with fewer features and less computational complexity than WFA and FFA.