This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and\ud a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other\ud segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician\ud marked-up boundaries as ground truth
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