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Lesion boundary segmentation using level set methods

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

Abstract

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

Topics: G400 Computer Science
Year: 2009
OAI identifier: oai:eprints.lincoln.ac.uk:1658

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Citations

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