Optimizing Sharpness Measure for Bright Lesion Detection in Retinal Image Analysis

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Title Optimizing Sharpness Measure for Bright Lesion Detection in Retinal Image Analysis
Author Lam, Benson; Gao, Yongsheng; Liew, Alan Wee-Chung
Publication Title Proceedings 2009 Digital Image Computing: Techniques and Applications DICTA 2009
Editor Hao Shi, Yanchun Zhang, Murk J. Bottema, Brian C. Lovell, Anthony J. Maeder
Year Published 2009
Place of publication Australia
Publisher IEEE Conference Publishing Services
Abstract Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/DICTA.2009.14
Copyright Statement Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 978-0-7695-3866-2
Conference name Digital Image Computing: Techniques and Applications, DICTA2009
Location Melbourne, Australia
Date From 2009-12-01
Date To 2009-12-03
URI http://hdl.handle.net/10072/30006
Date Accessioned 2010-03-05
Date Available 2010-06-03T09:25:57Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Image Processing
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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