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Computer-aided detection of lung nodules using outer surface features

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Demir, Öndera; * | Yılmaz Çamurcu, Alib

Affiliations: [a] Computer Engineering Department, Technology Faculty, Marmara University, 34722 Kadikoy, İstanbul, Turkey | [b] Computer Engineering Department, Engineering Faculty, Fatih Sultan Mehmet Vakif University, 34445 Beyoglu, İstanbul, Turkey

Correspondence: [*] Address for correspondence: Önder Demir, Computer Engineering Department, Technology Faculty, Marmara University, 34722 Kadikoy, İstanbul, Turkey. Tel.: +90 (216) 3365770/1226; Fax: +90 (216) 337 89 87; E-mail: [email protected].

Keywords: Lung nodule detection, CAD systems, texture features, medical image processing, classification

DOI: 10.3233/BME-151418

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1213-S1222, 2015

Published: 2015
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In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.

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