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Proceedings Paper

Automated detection of pulmonary nodules in CT images with support vector machines
Author(s): Lu Liu; Wanyu Liu; Xiaoming Sun
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Paper Abstract

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Paper Details

Date Published: 12 January 2009
PDF: 6 pages
Proc. SPIE 7133, Fifth International Symposium on Instrumentation Science and Technology, 713326 (12 January 2009); doi: 10.1117/12.810633
Show Author Affiliations
Lu Liu, Harbin Institute of Technology (China)
Wanyu Liu, Harbin Institute of Technology (China)
Xiaoming Sun, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 7133:
Fifth International Symposium on Instrumentation Science and Technology
Jiubin Tan; Xianfang Wen, Editor(s)

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