咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >Genetic programming-based feat... 收藏

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

为计算断层摄影术图象上的肺的小瘤的自动察觉的基因基于编程的特征变换和分类

作     者:Choi, Wook-Jin Choi, Tae-Sun 

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.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分