NDLI: Computer-aided detection of pulmonary nodules using genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Wook-Jin Choi Tae-Sun Choi
Copyright Year	2010
Description	Author affiliation: Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 261 Cheomdan Gwagiro, Buk-Gu, 500-712, Republic of Korea (Wook-Jin Choi; Tae-Sun Choi)
Abstract	This paper describes a novel nodule detection method that enhances false positive reduction. Lung region is extracted from CT image sequence using adaptive thresholding and 18-connectedness voxel labelling. In the extracted lung region, nodule candidates are detected using adaptive multiple thresholding and rule based classifier. After that, we extract the 3D and 2D features from nodule candidates. The nodule candidates are then classified using genetic programming (GP) based classifier. In this work, a new fitness function is proposed to generate optimal adaptive classifier. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The classifier was trained and evaluated using two independent dataset and whole dataset. The proposed method reduced the false positives in nodule candidates and achieved 92% detection rate with 6.5 false positives per scan.
Starting Page	4353
Ending Page	4356
File Size	244524
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5652369
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Lungs Three dimensional displays Computed tomography Cancer Databases Labeling CAD development HRCT Pulmonary Nodule Lung Nodule detection
Content Type	Text
Resource Type	Article

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