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

  • Authors:

  • Wook-Jin Choi;Tae-Sun Choi

  • Affiliations:

  • Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan-gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea;Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan-gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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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 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.