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

  • Patient Facing Systems
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Abstract

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.

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Acknowledgements

This work was supported in part by National 863 project of China (SS2015AA020109), National Natural Science Foundation of China (No. 61502472 and No. 31300816) and STS funding from Chinese Academy of Sciences (JCYJ20140417113430655 and JCYJ20140417113430619).

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    Authors and Affiliations

    1. Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China

      Ji-kui Liu, Pu Wang & Ye li

    2. Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China

      Hong-yang Jiang, Meng-di Gao, Yu Wang & He Ma

    3. Software School, North China University of Water Resources and Electric Power, Zhengzhou, 450045, Henan, China

      Chen-guang He

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    1. Ji-kui Liu
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    Correspondence to He Ma or Ye li.

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    This article is part of the Topical Collection on Patient Facing Systems

    Ji-kui Liu and Hong-yang Jiang contributed equally to this work.

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    Liu, Jk., Jiang, Hy., Gao, Md. et al. An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images. J Med Syst 41, 30 (2017). https://doi.org/10.1007/s10916-016-0669-0

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