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A robust lesion boundary segmentation algorithm using level set methods

By Elizabeth Massey, Andrew Hunter, James Lowell and David Steel


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\ud by a gradient map built using a combination of histogram equalization and robust statistics. The stopping\ud mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness\ud measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object.\ud We compare the proposed method against five other\ud segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician\ud demarcated boundaries as ground truth

Topics: G400 Computer Science, G740 Computer Vision
Year: 2009
DOI identifier: 10.1007/978-3-642-03891-4_81
OAI identifier: oai:eprints.lincoln.ac.uk:2131

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