A novel method for optic disc localization using fast circlet transform and Chan-Vese segmentation.

Accurate localization and segmentation of the optic disc (OD) are considered crucial for the early detection of ophthalmic diseases such as glaucoma and diabetic retinopathy. Challenges such as image quality variability, high background noise, and insufficient edge information are often encountered by existing methods. To address these issues, an adaptive framework is proposed in which Fast Circlet Transformation (FCT) is combined with entropy-based features derived from retinal blood vessels for robust OD localization. Minkowski weighted K-means clustering is utilized to dynamically assess feature importance, thereby enhancing resilience to dataset variations. Following localization, partial differential equation-based image inpainting is employed for blood vessel removal, and OD segmentation is refined using the Chan-Vese active contour model. The method's localization efficacy is demonstrated through extensive evaluations across multiple public datasets (DRISHTI-GS, DRIONS-DB, IDRID, and ORIGA), and segmentation performance metrics, including Dice coefficients of 0.94-0.95 and Jaccard indices of 0.9, are achieved on the ORIGA and DRISHTI-GS datasets. Through these results, the robustness and generalizability of the proposed method for clinical applications in retinal image analysis are highlighted.