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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 Joordens
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.

History

Event

IEEE Intelligent Computing and Intelligent Systems. Conference (2011 : Guangzhou, China)

Pagination

447 - 451

Publisher

IEEE

Location

Guangzhou, China

Place of publication

Piscataway, N.J.

Start date

2011-11-18

End date

2011-11-20

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2011, IEEE

Title of proceedings

ICIS 2011 : Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems

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