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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 241647, 7 pages
Research Article

Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images

1CEME, National University of Science and Technology, Islamabad 46000, Pakistan
2MCS, National University of Science and Technology, Islamabad 46000, Pakistan

Received 26 August 2014; Revised 4 November 2014; Accepted 4 November 2014; Published 25 November 2014

Academic Editor: Issam El Naqa

Copyright © 2014 Saleem Iqbal 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.


Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of “Lung Image Database Consortium-Image Database Resource Initiative” taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible.