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Type: Journal article
Title: Automated detection of lung nodules in computed tomography images: a review
Author: Lee, S.
Kouzani, A.
Hu, E.
Citation: Machine Vision and Applications: an international journal, 2012; 23(1):151-163
Publisher: Springer-Verlag
Issue Date: 2012
ISSN: 0932-8092
Statement of
S.L.A. Lee, A.Z. Kouzani and E.J. Hu
Abstract: 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.
Keywords: Computed tomography
Lung images
Pulmonary nodules
Automated detection
Performance evaluation
Rights: © Springer-Verlag 2010
DOI: 10.1007/s00138-010-0271-2
Appears in Collections:Aurora harvest
Environment Institute publications
Mechanical Engineering publications

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