Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images

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. I...

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Vydáno v:Information sciences Ročník 212; s. 57 - 78
Hlavní autoři: Choi, Wook-Jin, Choi, Tae-Sun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.12.2012
Témata:
CAD
Classification
Genetic programming
Genetics
Lungs
Mathematical analysis
Mathematical models
Nodules
Pulmonary nodule detection
CT
Pulmonary nodule detection
Genetic programming
CAD
ISSN:0020-0255, 1872-6291
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Shrnutí: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 labeling. 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 94.1% sensitivity at 5.45 false positives per scan.
Bibliografie:ObjectType-Article-2
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content type line 23
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2012.05.008