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作者机构:GIST Sch Informat & Mechatron Kwangju 500712 South Korea
出 版 物:《INFORMATION SCIENCES》 (信息科学)
年 卷 期:2012年第212卷
页 面:57-78页
核心收录:
学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:Bio Imaging Research Center at the Gwangju Institute of Science and Technology (GIST) Korea
主 题:CT Pulmonary nodule detection CAD Genetic programming
摘 要: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. (C) 2012 Elsevier Inc. All rights reserved.