Enhancing Retinal OCT Scans via Metaheuristic-Driven Bayesian Speckle Denoising

Optical Coherence Tomography (OCT) is an imaging technique that enables medical experts to obtain transverse scans of various small tissues, including relevant eye elements, such as the retina. The analysis of retinal OCT scans holds significant clinical relevance as it provides physicians with anatomical and pathological insights. However, speckle noise, inherent to OCT’s operational nature, negatively impacts image quality and hinders precise interpretation and diagnosis. In this regard, this paper presents the Metaheuristic-Driven Bayesian Speckle Denoising (MDBSD) framework: a fusion of a metaheuristic-based optimization using the Optimized Bayesian Non-local means with block selection (OBNLM) as the transformation function while introducing a specific fitness function for the evaluation of the potential solutions. Different classical metaheuristic algorithms are utilized and applied to a public retinal OCT dataset, followed by a numerical evaluation using contrast and denoising performance metrics to validate this framework. Regardless of the metaheuristic technique used for optimization, enhanced images present a notorious scan denoising while keeping retinal layers’ edges quite delimited, proving this framework’s efficiency. Additionally, future research directions are outlined for further advancement in this area.