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Automated identification of lung nodules

Lee, S. L. A., Kouzani, A. Z. and Hu, E. J. 2008, Automated identification of lung nodules, in MMSP 2008 : Proceedings of IEEE 10th International Workshop on Multimedia Signal Processing, IEEE, Piscataway, N.J., pp. 497-502.

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Title Automated identification of lung nodules
Author(s) Lee, S. L. A.
Kouzani, A. Z.
Hu, E. J.
Conference name IEEE International Workshop on Multimedia Signal Processing (10th : 2008 : Cairns, Qld.)
Conference location Cairns, Qld.
Conference dates 8-10 October 2008
Title of proceedings MMSP 2008 : Proceedings of IEEE 10th International Workshop on Multimedia Signal Processing
Editor(s) Feng, David
Sikora, Thomas
Siu, W.C.
Zhang, Jian
Guan, Ling
Dugelay, Jean-Luc
Wu, Qiang
Li, Wanqing
Publication date 2008
Conference series International Workshop on Multimedia Signal Processing
Start page 497
End page 502
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary A system that can automatically detect nodules within lung images may assist expert radiologists in interpreting the abnormal patterns as nodules in 2D CT lung images. A system is presented that can automatically identify nodules of various sizes within lung images. The pattern classification method is employed to develop the proposed system. A random forest ensemble classifier is formed consisting of many weak learners that can grow decision trees. The forest selects the decision that has the most votes. The developed system consists of two random forest classifiers connected in a series fashion. A subset of CT lung images from the LIDC database is employed. It consists of 5721 images to train and test the system. There are 411 images that contained expert- radiologists identified nodules. Training sets consisting of nodule, non-nodule, and false-detection patterns are constructed. A collection of test images are also built. The first classifier is developed to detect all nodules. The second classifier is developed to eliminate the false detections produced by the first classifier. According to the experimental results, a true positive rate of 100%, and false positive rate of 1.4 per lung image are achieved.
ISBN 9781424422951
Language eng
Field of Research 080104 Computer Vision
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018295

Document type: Conference Paper
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.