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  1. IEEE International Workshop on Computational Intelligence In Medical Imaging (CIMI).
  2. 2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging
  3. Optic disc segmentation by incorporating blood vessel compensation
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2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI)
2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging
Front cover
Copyright page
Table of contents
IEEE CIMI 2011 committee
Optic disc segmentation by incorporating blood vessel compensation
Brain MRI classification using an ensemble system and LH and HL wavelet sub-bands features
A probabilistic network based similiarity measure for cerebral tumors MRI cases retrieval
Application of particle swarm optimization and snake model hybrid on medical imaging
Evaluation of various evolutionary methods for medical image registration
Author index

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Optic disc segmentation by incorporating blood vessel compensation

Content Provider IEEE Xplore Digital Library
Author Salazar-Gonzalez, A.G. Yongmin Li Xiaohui Liu
Copyright Year 2011
Description Author affiliation: Department of Information, Systems and Computing, Brunel University, Uxbridge, West London UB8 3PH (Salazar-Gonzalez, A.G.; Yongmin Li; Xiaohui Liu)
Abstract Glaucoma is one of the main causes of blindness worldwide. Segmentation of vascular system and optic disc is an important step in the development of an automatic retinal screening system. In this paper we present an unsupervised method for the optic disc segmentation. The main obstruction in the optic disc segmentation process is the presence of blood vessels breaking the continuity of the object. While many other methods have addressed this problem trying to eliminate the vessels, we have incorporated the blood vessel information into our formulation. The blood vessel inside of the optic disc are used to give continuity to the object to segment. Our approach is based on the graph cut technique, where the graph is constructed considering the relationship between neighboring pixels and by the likelihood of them belonging to the foreground and background from prior information. Our method was tested on two public datasets, DIARETDB1 and DRIVE. The performance of our method was measured by calculating the overlapping ratio (Oratio), sensitivity and the mean absolute distance (MAD) with respect to the manually labeled images. Experimental results demonstrate that our method outperforms other methods on these datasets.
Starting Page 1
Ending Page 8
File Size 1263149
Page Count 8
File Format PDF
ISBN 9781612843346
e-ISBN 9781612843360
DOI 10.1109/CIMI.2011.5952040
Language English
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date 2011-04-11
Publisher Place France
Access Restriction Subscribed
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword Image segmentation Retinal images Vessel segmentation Graph cut technique Blood vessels Optical imaging Adaptive optics Optic disc segmentation Optical sensors Pixel Biomedical imaging
Content Type Text
Resource Type Article
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