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. I. INTRODUCTION PTICAL coherence tomography (OCT) imaging of the eye has rapidly become one of the most important imaging modalities for the diagnosis and management of retinal diseases. Current state of the art Spectral Domain OCT (SD-OCT) imaging systems provide clinicians with micron-level resolution [1-3]. This technology has moved rapidly from engineering to clinical implementation and tools to quantify and assist users with visualization and interpretation of the data generated are still developing [4-7]. Quantitative analysis of biomedical image data plays an increasingly important role in medical decision making. Feature detection may be an important component of tumor identification in breast cancer [8-13] and image segmentation may be critical information that helps clinicians to decide whether or not medical therapy is halting the progression of optic nerve degeneration in glaucoma [14, 15]. Biomedical image information is also increasingly used as guidance for surgical treatments [16]. Each of these applications relies on various elements of image processing that may include feature detection, segmentation, registration, and denoising as well as other operations. Reliable quantitative analysis of image data is an important step in deriving actionable information from imaging data. Manuscript received April 15, 2011. P. M. Kulkarni, Electrical and Computer Engineering, University of Houston, Houston, TX 77204 USA. (e-mail: prathameshmkulkarni@gmail.com). D. C. Lozano, University of Houston College of Optometry, Houston, TX 77204-2020 USA (e-mail: lozano.dc@gmail.com). G. Zouridakis, Engineering Technology, Computer Science, and Electrical and Computer Engineering, University of Houston, Houston, TX 77204 USA (e-mail: zouridakis@uh.edu). M. Twa, University of Houston College of Optometry, Houston, TX 77204-2020 USA (713-743-2996; e-mail: mdtwa@uh.edu). There are numerous studies that use synthetic or phantom images as a benchmark to enable quantitative comparisons of image processing operations. For example, the modified Shepp-Logan phantom is frequently used as a reference for CT and MRI image processing evaluations. Despite its simplicity (or perhaps because of it) this model has been a highly useful tool for quantitative analysis of image processing methods in MRI and CT data [17, 18]. Phantom images have also been used for validation of image registration [20] and segmentation algorithms [21, 22] for ultrasound data. However, these synthetic images were not derived from real data. In this research, we derive a synthetic model for retinal OCT data from real image data. The objective of this research was to develop a model of retinal SD-OCT data that could be used as a benchmark for similar quantitative research with OCT image data. The goal was to provide a standardized synthetic data set for comparison of various image processing methods. II. METHODS A. Data Fourteen high-quality B-Scan images were selected from within three different C-Scan volumes, each from a different animal. All parameters of the model were derived using these fourteen images. To insure that all B-Scan images had comparable thickness parameters for each layer, the B-Scan images were selected from regions adjacent to the optic nerve head (Figure 1). B. Manual segmentation Each B-Scan image included in the model was manually segmented by domain experts using a custom designed image annotation tool. The segmentation procedure involved delineating the boundaries for seven different retinal layers and demarcating blood vessels in the image. The seven layers included in the segmentations were: Nerve Fiber Layer-Ganglion Cell Layer (NFL-GCL), Inner Plexiform Layer (IPL), Inner Nuclear Layer (INL), Outer Plexiform Layer (OPL), Outer Nuclear Layer (ONL), Photo Receptor Layer (PRL) and Retinal Pigmented Epithelium (RPE). Each of these layers is identified separately in Figure 2. Manual segmentation of these multiple images was the basis for the intensity, gradient and contour parameters of the synthetic image model. C. Intensity modeling Intensity distribution models were derived for seven retinal layers by analyzing the distributions for these layers across A Statistical Model of Retinal Optical Coherence Tomography Image Data Prathamesh Kulkarni, Diana Lozano, George Zouridakis, and Michael Twa O 978-1-4244-4122-8/11/$26.00 ©2011 IEEE 6127 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30 - September 3, 2011