Automation of Cluster Extraction in Fundus Autofluorescence Images of Geographic Atrophy

Abstract

The build-up of lipofuscin—an age-associated biomarker referred to as hyperfluorescence—is considered a precursor in the progression of geographic atrophy (GA). Prior studies have attempted to classify hyperfluorescent regions to explain varying rates of GA progression. In this study, digital image processing and unsupervised learning were used to (1) completely automate the extraction of hyperfluorescent regions from images, and (2) evaluate prospective patterns and groupings of hyperfluorescent areas associated with varying levels of GA progression. Patterns were determined by clustering methods, such as k-Means, and performance was evaluated using metrics such as the Silhouette Coefficient (SC), the Davies–Bouldin Index (DBI), and the Calinski–Harabasz Index (CHI). Automated extraction of hyperfluorescent regions was carried out using pseudocoloring techniques. The approach revealed three distinct types of hyperfluorescence based on color intensity changes: early-stage hyperfluorescence, intermediate-stage hyperfluorescence, and late-stage hyperfluorescence, with the early and late stages having three additional subclassifications that could explain varying levels of GA progression. The performance metrics for early-stage hyperfluorescence were SC = 0.597, DBI = 0.915, and CHI = 186.989. For late-stage hyperfluorescence, SC = 0.593, DBI = 1.013, and CHI = 217.325. No meaningful subclusters were identified for the intermediate-stage hyperfluorescence, possibly because it is a transitional phase of hyperfluorescence progression.

Keywords

• geographic atrophy
• age-related macular degeneration
• unsupervised learning
• machine learning
• automation