When our Fourier analysis procedure is applied to the data of each individual and the linear discriminant analysis procedure is applied, each eye can be classified as glaucomatous or normal on the basis of the resultant discriminant function. The stepwise procedure yields an equation consisting only of terms that contribute significantly to the discrimination of glaucoma from healthy eyes. For the OCT device, the function was 0.041 × DC + 0.077 × F2 amplitude + 0.648 × F3 phase − 6.578. For the GDx device, the function was 0.260 × F2 amplitude + 1.366 × F12 amplitude + 0.681× F12 phase − 1.912 × F13 amplitude + 0.798 × F14 phase − 5.942. The most important variables in the equations (in terms of discriminative power) were, for OCT, the DC level, then, approximately equally, F2 amplitude and F3 phase, whereas for GDx they were F2 amplitude and F14 phase. Only the amplitude of the second Fourier coefficient is present in both functions. However, because the 2 functions are based on a different number of input coefficients, it is not surprising that the relevant coefficients are different (ie, there are only 6 amplitude and phase coefficients for the OCT data, and there are 16 for the GDx data). From these functions, sensitivity and specificity values and associated ROC curves were obtained. The ROC curves from application of the Fourier metrics and the most prevalent metrics in the literature are shown in Figure 3 for the GDx device and in Figure 4 for the OCT device. For the GDx data, the areas under the curve for the GDx number, the UCSD LDF, 28 and the Fourier-based LDF were 0.734, 0.773, and 0.928, respectively (SEM, 0.056, 0.052, and 0.029, respectively). For the GDx device, the Fourier-based LDF performed significantly better, with the area under the ROC curve significantly larger than that obtained from the GDx number or the UCSD LDF (P<.05). For the OCT device, we evaluated the standard measures of mean thickness, inferior sector thickness, and the Fourier-based LDF and obtained areas under the ROC curves of 0.872, 0.888, and 0.925, respectively (SEM, 0.041, 0.038, and 0.028, respectively), as shown in Figure 4.Again, the discrimination ability (ROC area) was greater for the Fourier-based LDF measure (but not significantly [P>.05]). The split-half reliability analysis (Table 2) shows that these findings are reproducible. When the various measures are tested on the half-size independent samples, the Fourier measures still outperform the GDx number and the UCSD LDF, with best performance for the OCT Fourier-based LDF and poorest performance for the GDx number.