A statistical model of retinal optical coherence tomography image data

Abstract

Optical coherence tomography (OCT) is an important mode of biomedical imaging for the diagnosis and management of ocular disease. Here we report on the construction of a synthetic retinal OCT image data set that may be used for quantitative analysis of image processing methods. Synthetic image data were generated from statistical characteristics of real images (n = 14). Features include: multiple stratified layers with representative thickness, boundary gradients, contour, and intensity distributions derived from real data. The synthetic data also include retinal vasculature with typical signal obscuration beneath vessels. This synthetic retinal image can provide a realistic simulated data set to help quantify the performance of image processing algorithms.