General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling

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Title General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling
Author Lam, Benson; Gao, Yongsheng; Liew, Alan Wee-Chung
Journal Name I E E E Transactions on Medical Imaging
Year Published 2010
Place of publication United States
Publisher I E E E
Abstract Detecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remove dark lesions which have an intensity structure different from the line-shaped vessels in a retina. The locally normalized concavity measure is designed to deal with unevenly distributed noise due to the spherical intensity variation in a retinal image. These concavity measures are combined together according to their statistical distributions to detect vessels in general retinal images. Very encouraging experimental results demonstrate that the proposed method consistently yields the best performance over existing state-of-the-art methods on the abnormal retinas and its accuracy outperforms the human observer, which has not been achieved by any of the state-of-the-art benchmark methods. Most importantly, unlike existing methods, the proposed method shows very attractive performances not only on healthy retinas but also on a mixture of healthy and pathological retinas.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/TMI.2010.2043259
Copyright Statement Copyright 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Volume 29
Issue Number 7
Page from 1369
Page to 1381
ISSN 0278-0062
Date Accessioned 2010-11-17
Date Available 2011-02-09T06:41:48Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Image Processing
URI http://hdl.handle.net/10072/35458
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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