An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images

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• 作者：Ji-kui Liu ; Hong-yang Jiang ; Meng-di Gao ; Chen-guang He…
• 关键词：Computer aided diagnosis (CAD) ; Pulmonary nodule detection ; Ensemble classifier ; LIDC
• 刊名：Journal of Medical Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：41
• 期：2
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Health Informatics; Health Informatics; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：Springer US
• ISSN：1573-689X
• 卷排序：41

文摘

Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.