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Empirical evaluation of segmentation algorithms for lung modelling

Lee, S. L. A., Kouzani, A. Z. and Hu, E. J. 2008, Empirical evaluation of segmentation algorithms for lung modelling, in SMC 2008 : Proceedings of 2008 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 719-724.

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Title Empirical evaluation of segmentation algorithms for lung modelling
Author(s) Lee, S. L. A.
Kouzani, A. Z.
Hu, E. J.
Conference name IEEE International Conference on Systems, Man and Cybernetics (2008 : Singapore)
Conference location Singapore
Conference dates 12-15 October 2008
Title of proceedings SMC 2008 : Proceedings of 2008 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2008
Conference series International Conference on Systems, Man and Cybernetics
Start page 719
End page 724
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) CT lung images
image segmentation
Summary Lung modelling has emerged as a useful method for diagnosing lung diseases. Image segmentation is an important part of lung modelling systems. The ill-defined nature of image segmentation makes automated lung modelling difficult. Also, low resolution of lung images further increases the difficulty of the lung image segmentation. It is therefore important to identify a suitable segmentation algorithm that can enhance lung modelling accuracies. This paper investigates six image segmentation algorithms, used in medical imaging, and also their application to lung modelling. The algorithms are: normalised cuts, graph, region growing, watershed, Markov random field, and mean shift. The performance of the six segmentation algorithms is determined through a set of experiments on realistic 2D CT lung images. An experimental procedure is devised to measure the performance of the tested algorithms. The measured segmentation accuracies as well as execution times of the six algorithms are then compared and discussed.
ISBN 9781424423842
Language eng
Field of Research 080104 Computer Vision
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018306

Document type: Conference Paper
Collections: School of Engineering and Information Technology
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