Adaptive Super-Candidate Based Approach for Detection and Classification of Drusen on Retinal Fundus Images
Abstract
Identification and characterization of drusen is essential for the severity assessment of age-related macular degeneration (AMD). Presented here is a novel super-candidate based approach, combined with robust preprocessing and adaptive thresholding for detection of drusen, resulting in accurate segmentation with the mean lesion-level overlap of 0.75, even in cases with non-uniform illumination, poor contrast and con- founding anatomical structures. We also present a feature based lesion- level discrimination analysis between hard and soft drusen. Our method gives sensitivity of 80% for high specificity above 90% and high sensitivity of 95% for specificity of 70% on representative pathological databases (STARE and ARIA) for both detection and discrimination.
How to Cite:
Sundaresan, V. & Ram, K. & Selvaraj, K. & Joshi, N. & Sivaprakasam, M., (2015) “Adaptive Super-Candidate Based Approach for Detection and Classification of Drusen on Retinal Fundus Images”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop 2(2015), 81-88. doi: https://doi.org/10.17077/omia.1030
Rights: Copyright © 2015 Vaanathi Sundaresan, Keerthi Ram, Kulasekaran Selvaraj, Niranjan Joshi and Mohanasankar Sivaprakasam
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