Lesion Detection from Contrast Enhancement Algorithms in Retinal Imaging Datasets: Graph Analysis

Abstract

The accurate detection of retinal lesions is a fundamental requirement for the early diagnosis and management of severe ocular diseases such as diabetic retinopathy and age-related macular degeneration. While conventional contrast enhancement algorithms are routinely applied to improve image quality and highlight pathological features, their impact on the structural and topological integrity of the underlying biological features remains insufficiently quantified. This paper introduces a comprehensive framework that utilizes graph analysis to predict the efficacy of lesion detection across various contrast enhancement algorithms applied to retinal imaging datasets. By transforming retinal images into complex graph structures where nodes represent superpixels and edges denote structural similarities, we quantify topological alterations induced by enhancement techniques. We extract sophisticated network metrics, including centrality, clustering coefficients, and modularity, to establish a predictive relationship between graph topology and downstream lesion detection accuracy. Extensive empirical evaluations reveal that contrast enhancement techniques preserving the original topological connectivity yield superior detection rates compared to those that indiscriminately maximize local contrast. The proposed graph-based analytical approach provides a robust and interpretable mechanism to evaluate preprocessing algorithms, thereby advancing the reliability of automated diagnostic systems in ophthalmology.