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.

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Title Automated detection of lung nodules in computed tomography images: a review
Author(s) Lee, Shu Ling (Alycia)
Kouzani, Abbas Z.ORCID iD for Kouzani, Abbas Z.
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
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
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