Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes

Abstract

Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diabetic retinopathy and other ocular diseases. But accurate identification is challenging due to varying contrast, color irregularities, anatomical structure and the presence of lesions near the macula in fundus images. Existing methods often struggle to maintain accuracy in these complex conditions, particularly when lesions obscure vital regions. To overcome these limitations, this paper introduces a novel adaptive ellipse-template-based approach for fovea localization, leveraging mathematical modeling of blood vessel (BV) trajectories and optic disc (OD) positioning. Unlike traditional fixed-template model, our method dynamically adjusts the ellipse parameters based on OD diameter, ensuring a generalized and adaptable template. This flexibility enables consistent detection performance, even in challenging images with significant lesion interference. Extensive validation on ten publicly available databases, including MESSIDOR, DRIVE, DIARETDB0, DIARETDB1, HRF, IDRiD, HEIMED, ROC, GEI, and NETRALAYA, demonstrates a superior detection efficiency of 99.5%. Additionally, the method achieves a low mean Euclidean distance of 13.48 pixels with a standard deviation of 15.5 pixels between the actual and detected fovea locations, highlighting its precision and reliability. The proposed approach significantly outperforms conventional template-based and deep learning methods, particularly in lesion-rich and low-contrast conditions. It is computationally efficient, interpretable, and robust, making it a valuable tool for automated retinal image analysis in clinical settings.