Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Choi201257,
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author = "Wook-Jin Choi and Tae-Sun Choi",
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title = "Genetic programming-based feature transform and
classification for the automatic detection of pulmonary
nodules on computed tomography images",
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journal = "Information Sciences",
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volume = "212",
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pages = "57--78",
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year = "2012",
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month = "1 " # dec,
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2012.05.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025512003362",
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keywords = "genetic algorithms, genetic programming, CT, Pulmonary
nodule detection, CAD",
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abstract = "An effective automated pulmonary nodule detection
system can assist radiologists in detecting lung
abnormalities at an early stage. In this paper, we
propose a novel pulmonary nodule detection system based
on a genetic programming (GP)-based classifier. The
proposed system consists of three steps. In the first
step, the lung volume is segmented using thresholding
and 3D-connected component labelling. In the second
step, optimal multiple thresholding and rule-based
pruning are applied to detect and segment nodule
candidates. In this step, a set of features is
extracted from the detected nodule candidates, and
essential 3D and 2D features are subsequently selected.
In the final step, a GP-based classifier (GPC) is
trained and used to classify nodules and non-nodules.
GP is suitable for detecting nodules because it is a
flexible and powerful technique; as such, the GPC can
optimally combine the selected features, mathematical
functions, and random constants. Performance of the
proposed system is then evaluated using the Lung Image
Database Consortium (LIDC) database. As a result, it
was found that the proposed method could significantly
reduce the number of false positives in the nodule
candidates, ultimately achieving a 94.1percent
sensitivity at 5.45 false positives per scan.",
- }
Genetic Programming entries for
Wook-Jin Choi
Tae Sun Choi
Citations