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Diagnostics 2016, 6(1), 13; doi:10.3390/diagnostics6010013

Auto Diagnostics of Lung Nodules Using Minimal Characteristics Extraction Technique

1
Department of Digital Image Processing, Faculty of Biomedical Engineering, Southeast University, Nanjing 210096, China
2
Department of Electrical and Computer Engineering, Karary University, Khartoum 12304, Sudan
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Kjaer
Received: 20 January 2016 / Revised: 19 February 2016 / Accepted: 24 February 2016 / Published: 4 March 2016
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Abstract

Computer-aided detection (CAD) systems provide useful tools and an advantageous process to physicians aiming to detect lung nodules. This paper develops a method composed of four processes for lung nodule detection. The first step employs image acquisition and pre-processing techniques to isolate the lungs from the rest of the body. The second stage involves the segmentation process using a 2D algorithm to affect every layer of a scan eliminating non-informative structures inside the lungs, and a 3D blob algorithm associated with a connectivity algorithm to select possible nodule shape candidates. The combinations of these algorithms efficiently eliminate the high rates of false positives. The third process extracts eight minimal representative characteristics of the possible candidates. The final step utilizes a support vector machine for classifying the possible candidates into nodules and non-nodules depending on their features. As the objective is to find nodules bigger than 4mm, the proposed approach demonstrated quite encouraging results. Among 65 computer tomography (CT) scans, 94.23% of sensitivity and 84.75% in specificity were obtained. The accuracy of these two results was 89.19% taking into consideration that 45 scans were used for testing and 20 for training. The rate of false positives was 0.2 per scan. View Full-Text
Keywords: lung nodule; segmentation; image processing; nodule characteristics lung nodule; segmentation; image processing; nodule characteristics
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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. (CC BY 4.0).

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MDPI and ACS Style

Peña, D.M.; Luo, S.; Abdelgader, A.M.S. Auto Diagnostics of Lung Nodules Using Minimal Characteristics Extraction Technique. Diagnostics 2016, 6, 13.

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