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Evaluation of segmentation algorithms for extraction of RNFL in OCT images
conference contribution
posted on 2011-01-01, 00:00 authored by K Kipli, Abbas KouzaniAbbas Kouzani, Yong XiangYong Xiang, Matthew JoordensMatthew JoordensThe 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.
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Event
IEEE Intelligent Computing and Intelligent Systems. Conference (2011 : Guangzhou, China)Pagination
447 - 451Publisher
IEEELocation
Guangzhou, ChinaPlace of publication
Piscataway, N.J.Start date
2011-11-18End date
2011-11-20Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2011, IEEETitle of proceedings
ICIS 2011 : Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent SystemsUsage metrics
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