A Drusen volume quantification method based on a segmentation algorithm

Abstract

Age Related Macular Degeneration (ARMD) is a frequent disease appearing mainly in elderly people, commonly diagnosed by ophthalmologists during patient examination. ARMD is characterized by the accumulation of extra cellular materials under retina seen in retina images as yellowish spots and called Drusen spots. Until now, ophthalmologists analysis has done manually, based only on qualitative aspects. However this process is highly dependent on the ophthalmologist and is very difficult to reproduce. In this paper we propose a methodology for an automatic Drusen analysis, which models each Drusen spot calculating important information like Drusen area, volume and location. The proposed methodology is a reproducible process independent of the clinician allowing the comparison of, images taken in different instants to evaluate treatment effectiveness and disease evolution. To achieve our objectives four different algorithms were used: the first divides the original image in smaller images grouping Drusen neighbours, the second algorithm is based in a Gaussian Blur filter and corrects the non-uniform illumination. The third algorithm based on labelling gradient paths and is responsible for giving Drusen spots location and intensity. The last algorithm is based in Levenberg-Marquardt methodology and is responsible for this modelling of each Drusen spot.