Evaluation of segmentation algorithms for extraction of RNFL in OCT images Kipli, Kuryati, Kouzani, Abbas Z., Xiang, Yong and Joordens, Matthew 2011, Evaluation of segmentation algorithms for extraction of RNFL in OCT images, in ICIS 2011 : Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, IEEE, Piscataway, N.J., pp. 447-451. Attached Files Name Description MIMEType Size Downloads Title Evaluation of segmentation algorithms for extraction of RNFL in OCT images Author(s) Kipli, Kuryati Kouzani, Abbas Z. orcid.org/0000-0002-6292-1214 Xiang, Yong orcid.org/0000-0003-3545-7863 Joordens, Matthew orcid.org/0000-0003-2253-4428 Conference name IEEE Intelligent Computing and Intelligent Systems. Conference (2011 : Guangzhou, China) Conference location Guangzhou, China Conference dates 18-20 Nov. 2011 Title of proceedings ICIS 2011 : Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems Editor(s) [Unknown] Publication date 2011 Conference series IEEE Intelligent Computing and Intelligent Systems Conference Start page 447 End page 451 Total pages 5 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) segmentation optical coherence tomography retinal nerve fiber layer glaucoma Summary The thickness of the retinal nerve fiber layer (RFNL) has become a diagnose measure for glaucoma assessment. To measure this thickness, accurate segmentation of the RFNL in optical coherence tomography (OCT) images is essential. Identification of a suitable segmentation algorithm will facilitate the enhancement of the RNFL thickness measurement accuracy. This paper investigates the performance of six algorithms in the segmentation of RNFL in OCT images. The algorithms are: normalised cuts, region growing, k-means clustering, active contour, level sets segmentation: Piecewise Gaussian Method (PGM) and Kernelized Method (KM). The performance of the six algorithms are determined through a set of experiments on OCT retinal images. An experimental procedure is used to measure the performance of the tested algorithms. The measured segmentation precision-recall results of the six algorithms are compared and discussed. Language eng Field of Research 090302 Biomechanical Engineering 080106 Image Processing Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2011, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30051055 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 07 Mar 2013, 12:36:24 EST Thu, 07 Mar 2013, 13:38:13 EST Thu, 28 Mar 2013, 14:36:46 EST Wed, 22 May 2013, 10:21:32 EST Thu, 23 May 2013, 08:57:33 EST Thu, 12 Sep 2013, 12:41:43 EST Fri, 08 Aug 2014, 11:37:41 EST Wed, 27 Aug 2014, 23:22:23 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 720 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Thu, 07 Mar 2013, 12:36:24 EST