Retinal Boundary Segmentation in OCT Images Using Active Contour Model

Abstract

Optical coherence tomography (OCT) imaging is a precise and significant approach in retinal diagnostics at the layer level. The diseased effect in the retina poses a barrier to a computational segmentation approach at the boundary layer level for defect evaluation and diagnosis. The noise in the computing approach misguides the layer segmentation and border edging operation. In these requirements, a novel segmentation algorithm based on a denoising technique is required. The many layers of OCT under the macula area of the eye are to be highlighted in this work. The proposed contour model is used to determine the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), inner segment (IS), outer segment (OS), and retinal pigment epithelium (RPE). As preprocessing approaches, the median filter and histogram equalization are utilized, and the canny edge detector, coupled with watershed thresholding, provides post-processing so that layers can be easily identified. For simulation purposes, the MATLAB R2017b version tool is used in this work.