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International Journal of Biomedical Imaging
Volume 2013, Article ID 517632, 11 pages
Research Article

Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans

1Bioimaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
2Urology and Nephrology Department, University of Mansoura, Mansoura 35516, Egypt
3Department of Computer Science, The University of Auckland 1142, Auckland, New Zealand
4Medical Imaging Division, Jewish Hospital, Louisville, KY 40202, USA
5Electrical and Computer Engineering Department, University of Louisville, KY 40292, USA

Received 9 September 2012; Revised 13 December 2012; Accepted 14 December 2012

Academic Editor: Kazunori Okada

Copyright © 2013 Ayman El-Baz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.