Comparison of shape-based analysis of retinal nerve fiber layer data obtained From OCT and GDx-VCC.

PURPOSE: To directly compare in 1 population: (1) the performance of Optical Coherence Tomograph (OCT) and GDx-Variable Corneal Compensator (VCC) when using Wavelet-Fourier Analysis (WFA) and Fast-Fourier Analysis (FFA), (2) the performance of these shape-based and standard metrics, and (3) the shape of the retinal nerve fiber layer (RNFL) temporal, superior, nasal, inferior, temporal (TSNIT) curves obtained by the 2 different devices. METHODS: RNFL estimates were obtained from 136 eyes of 136 individuals (73 healthy and 63 mild glaucoma). WFA and FFA with and without asymmetry measures were performed on the TSNIT RNFL estimates to identify glaucoma from healthy eyes. Performance of WFA, FFA, and the standard metrics of OCT (Inferior Average) and GDX-VCC (Nerve Fiber Indicator) was evaluated by calculating receiver operating characteristic area. Measurements were obtained at a custom radius (33 to 41 pixels) for GDx-VCC to match the OCT radius (1.73 mm). RESULTS: WFA and FFA shape analysis significantly improved performance of both OCT (0.937) and GDx-VCC (0.913) compared with Inferior Average and Nerve Fiber Indicator (0.852 and 0.833, respectively). With either shape-based or standard metrics, OCT performance was slightly, but not significantly, better than GDx-VCC performance. Comparison of RNFL curves revealed that the GDx-VCC curves were more jagged and the peaks shifted more nasally when compared with the OCT RNFL curves. CONCLUSIONS: Performance of both OCT and GDx-VCC devices are improved by shape-based analysis methods. Classification performance was greater when using WFA for the OCT, and greater with FFA for the GDx-VCC. Significant differences between the machines exist in the measured TSNIT thicknesses, possibly because of GDx-VCC's measurements being affected by polarization magnitude varying with angle.

Application of shape-based analysis methods to OCT retinal nerve fiber layer data in glaucoma.

PURPOSE: (1) To evaluate the performance of shape-based analysis [wavelet-Fourier analysis (WFA) and fast Fourier analysis (FFA)] applied to retinal nerve fiber layer (RNFL) thickness values obtained from the optical coherence tomograph (OCT) to discriminate healthy and glaucomatous eyes. (2) To compare the performance of the shape-based metrics to that of the standard OCT output measures (Inferior Average and Average Thickness). METHODS: RNFL values were obtained from 152 eyes of 152 individuals (83 healthy and 69 "mild"-stage perimetric glaucoma). WFA and FFA were performed on the RNFL values and linear discriminant functions for both were obtained using Fisher linear discriminant analysis. Performance was evaluated by calculating sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (ROC area). RESULTS: The ROC area of the shape-based methods [0.94 (WFA) and 0.88 (FFA)] was greater than that of OCT metrics [0.81 (Inferior Average) and 0.74 (Average Thickness)]. Specifically, WFAs performance was significantly better than both the FFA (P=0.009) and the Inferior Average (P=0.001). Inferior average performed significantly better than Average Thickness (P=0.006). CONCLUSIONS: The ability to differentiate glaucomatous from healthy eyes using stratus OCT measurements is improved by using these analysis methods that emphasize the shape of the RNFL thickness pattern.