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Type: Conference paper
Title: Empirical evaluation of segmentation algorithms for lung modelling
Author: Lee, S.
Kouzani, A.
Hu, E.
Citation: IEEE International Conference on Systems, Man and Cybernetics, 2008 : proceedings: pp.719-724
Publisher: IEEE
Publisher Place: Online
Issue Date: 2008
Series/Report no.: IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISBN: 9781424423842
ISSN: 1062-922X
Conference Name: IEEE International Conference on Systems, Man and Cybernetics (2008 : Singapore)
Statement of
S.L.A. Lee, A.Z. Kouzani, and E.J. Hu
Abstract: 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.
Keywords: CT lung images
image segmentation
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Rights: © Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
DOI: 10.1109/ICSMC.2008.4811363
Appears in Collections:Aurora harvest
Environment Institute publications
Mechanical Engineering conference papers

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