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

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@Article{Choi201257,
  author =       "Wook-Jin Choi and Tae-Sun Choi",
  title =        "Genetic programming-based feature transform and
                 classification for the automatic detection of pulmonary
                 nodules on computed tomography images",
  journal =      "Information Sciences",
  volume =       "212",
  pages =        "57--78",
  year =         "2012",
  month =        "1 " # dec,
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2012.05.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025512003362",
  keywords =     "genetic algorithms, genetic programming, CT, Pulmonary
                 nodule detection, CAD",
  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

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