Automated detection of lung nodules in computed tomography images: a review Lee, Shu Ling (Alycia), Kouzani, Abbas Z. and Hu, Eric J. 2012, Automated detection of lung nodules in computed tomography images: a review, Machine vision and applications, vol. 23, no. 1, pp. 151-163, doi: 10.1007/s00138-010-0271-2. Attached Files Name Description MIMEType Size Downloads Title Automated detection of lung nodules in computed tomography images: a review Author(s) Lee, Shu Ling (Alycia) Kouzani, Abbas Z. orcid.org/0000-0002-6292-1214 Hu, Eric J. Journal name Machine vision and applications Volume number 23 Issue number 1 Start page 151 End page 163 Total pages 13 Publisher Springer Place of publication Heidelberg, Germany Publication date 2012-05-14 ISSN 0932-8092 1432-1769 Keyword(s) computed tomography lung images pulmonary nodules automated detection performance evaluation Summary Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches. Language eng DOI 10.1007/s00138-010-0271-2 Field of Research 090609 Signal Processing Socio Economic Objective 920203 Diagnostic Methods HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2010, Springer-Verlag Persistent URL http://hdl.handle.net/10536/DRO/DU:30035314 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 01 Jun 2011, 17:49:53 EST Wed, 01 Jun 2011, 17:54:08 EST Thu, 02 Jun 2011, 11:20:32 EST Thu, 02 Jun 2011, 11:22:36 EST Thu, 02 Jun 2011, 11:24:19 EST Thu, 02 Jun 2011, 11:25:44 EST Fri, 17 Jun 2011, 13:24:20 EST Fri, 17 Jun 2011, 14:15:58 EST Wed, 25 Jan 2012, 15:56:09 EST Wed, 25 Jan 2012, 16:06:28 EST Thu, 23 Feb 2012, 13:38:11 EST Mon, 27 Feb 2012, 14:30:14 EST Mon, 27 Feb 2012, 14:34:18 EST Mon, 27 Feb 2012, 14:42:19 EST Wed, 07 Mar 2012, 11:05:26 EST Tue, 20 Mar 2012, 11:55:52 EST Tue, 20 Mar 2012, 11:56:25 EST Tue, 20 Mar 2012, 13:22:40 EST Tue, 20 Mar 2012, 13:24:53 EST Thu, 22 Mar 2012, 08:04:46 EST Thu, 22 Mar 2012, 08:07:13 EST Fri, 09 Aug 2013, 13:25:42 EST Fri, 09 Aug 2013, 13:26:21 EST Mon, 12 Aug 2013, 08:36:16 EST Fri, 13 Sep 2013, 09:04:10 EST Fri, 22 Aug 2014, 16:05:03 EST Tue, 03 Feb 2015, 12:48:03 EST Filtered Full Citation counts:   Cited 53 times in TR Web of Science Cited 68 times in Scopus Search Google Scholar Access Statistics: 983 Abstract Views, 21 File Downloads  -  Detailed Statistics Created: Wed, 01 Jun 2011, 17:49:38 EST by Sandra Dunoon