Drusen Detection in a Retinal Image Using Multi-level Analysis

Abstract

This paper concerns a method to automatically detect drusen in a retinal image without human supervision or interaction. We use a multi-level approach, beginning with classification at the pixel level and proceeding to the region level, area level, and then image level. This allows the lowest levels of classification to be tuned to detect even the faintest and most difficult to discern drusen, relying upon the higher levels of classification to use an ever broadening context to refine the segmentation. We test our methods on a set of 119 images containing all types of drusen as well as images containing no drusen or other potentially confusing lesions. Our overall correct detection rate is 87%.

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