Robust image quality evaluation in optical coherence tomography of skin using global, region-independent metrics

Abstract

Abstract Optical coherence tomography (OCT) is a powerful imaging modality for visualizing tissue microstructures, but its utility is often limited by artifacts such as speckle, intensity decay, and blurring. While numerous image enhancement algorithms have been proposed to address these issues, the field lacks robust, objective metrics to consistently quantify image quality, hindering fair comparison and development of such methods. Existing metrics, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), depend heavily on user-selected regions of interest, introducing substantial variability. In this work, we introduce a fully automated framework that segments OCT skin images into air, signal, and noise regions and defines three global image quality metrics: noise-free pixel ratio (NFPR), global SNR (gSNR), and global contrast (gCN). These metrics analyze the entire image without requiring ROI selection, enabling reproducible and user-independent evaluations that correlate with both acquisition parameters and human visual perception. Extensive validation on skin OCT datasets demonstrates that the proposed metrics provide more consistent and stable characterization of image quality compared to conventional ROI-based metrics under the tested conditions. These results suggest that the proposed framework can support objective evaluation of OCT image quality and facilitate the development and benchmarking of image enhancement methods.