Skip to main content

Advertisement

SpringerLink
Log in

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

  • Patient Facing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ferlay, J., Soerjomataram, I., Dikshit, R., et al., Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5):E359–E386, 2015.

    Article  CAS  PubMed  Google Scholar 

  2. Della, P. C., Cigarette smoking: the most widespread and insidious weapon of mass destruction. Arch. Med. Vet. 1(1):5, 2015.

    Google Scholar 

  3. Pesch, B., Kendzia, B., Gustavsson, P., et al., Cigarette smoking and lung cancer—relative risk estimates for the major histological types from a pooled analysis of case–control studies. Int. J. Cancer 131(5):1210–1219, 2012.

    Article  CAS  PubMed  Google Scholar 

  4. World Health Organization International, Tobacco smoke and involuntary smoking. Lyon, France, 2002.

    Google Scholar 

  5. Chen, X., Shao, S., Tian, Z., et al., Impacts of air pollution and its spatial spillover effect on public health based on China’s big data sample. J. Clean. Prod. 2016. doi:10.1016/j.jclepro.2016.02.119.

    Google Scholar 

  6. Diederich, S., Lentschig, M., Overbeck, T., et al., Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting. Eur. Radiol. 11(8):1345–1350, 2001.

    Article  CAS  PubMed  Google Scholar 

  7. Valente, I. R. S., Cortez, P. C., Neto, E. C., et al., Automatic 3D pulmonary nodule detection in CT images: a survey. Comput. Meth. Programs Biomed. 2015. doi:10.1016/j.cmpb.2015.10.006.

    Google Scholar 

  8. Swensen, S. J., Viggiano, R. W., Midthun, D. E., et al., Lung nodule enhancement at CT: multicenter study 1. Radiology 214(1):73–80, 2000.

    Article  CAS  PubMed  Google Scholar 

  9. Xu, D. M., van der Zaag-Loonen, H. J., Oudkerk, M., et al., Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up. Radiology 250(1):264–272, 2009.

    Article  PubMed  Google Scholar 

  10. Li, Q., Sone, S., and Doi, K., Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans. Med. Phys. 30(8):2040–2051, 2003.

    Article  PubMed  Google Scholar 

  11. Ozekes, S., and Osman, O., Computerized lung nodule detection using 3D feature extraction and learning based algorithms. J. Med. Syst. 34(2):185–194, 2010.

    Article  PubMed  Google Scholar 

  12. Noor, N. M., Than, J. C. M., Rijal, O. M., et al., Automatic lung segmentation using control feedback system: morphology and texture paradigm. J. Med. Syst. 39(3):1–18, 2015.

    Article  Google Scholar 

  13. Ma, Q., Ji, B., Jia, B., et al., Differential diagnosis of solitary pulmonary nodules using 99mTc-3P4-RGD2 scintigraphy. Eur. J. Nucl. Med. Mol. Imaging 38(12):2145–2152, 2011.

    Article  PubMed  Google Scholar 

  14. Firmino, M., Morais, A. H., and Mendoça, R. M., Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed. Eng. Online 13:1–16, 2014.

    Article  Google Scholar 

  15. Taşcı, E., and Uğur, A., Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs. J. Med. Syst. 39(5):1–13, 2015.

    Google Scholar 

  16. Peña, D. M., Luo, S., and Abdelgader, A., Auto diagnostics of lung nodules using minimal characteristics extraction technique. Diagnostics 6(1):13, 2016.

    Article  PubMed Central  Google Scholar 

  17. Penedo, M. G., Carreira, M. J., Mosquera, A., et al., Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. IEEE Trans. Med. Imaging 17(6):872–880, 1998.

    Article  CAS  PubMed  Google Scholar 

  18. Akram, S., Javed, M. Y., Akram, M. U., et al., Pulmonary nodules detection and classification using hybrid features from computerized tomographic images. J. Med. Imag. Health Inform. 6(1):252–259, 2016.

    Article  Google Scholar 

  19. Santos, A. M., de Carvalho Filho, A. O., Silva, A. C., et al., Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM. Eng. Appl. Artif. Intell. 36:27–39, 2014.

    Article  Google Scholar 

  20. Teramoto, A., and Fujita, H., Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int. J. Comput. Assist. Radiol. Surg. 8(2):193–205, 2013.

    Article  PubMed  Google Scholar 

  21. Gonçalves, L., Novo, J., and Campilho, A., Hessian based approaches for 3D lung nodule segmentation. Expert Syst. Appl. 61:1–15, 2016.

    Article  Google Scholar 

  22. Jiang, H. Y., Ma, H., Qian, W., et al., Risk analysis for pathological changes in pulmonary parenchyma based on lung computed tomography images. J. Comput. Assist. Tomogr. 40(3):357–363, 2016.

    Article  PubMed  Google Scholar 

  23. Messay, T., Hardie, R. C., and Rogers, S. K., A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 14(3):390–406, 2010.

    Article  PubMed  Google Scholar 

  24. Zhang, F., Song, Y., Cai, W., et al., Lung nodule classification with multilevel patch-based context analysis. IEEE Trans. Biomed. Eng. 61(4):1155–1166, 2014.

    Article  PubMed  Google Scholar 

  25. Feng, Z., Jiulun, F. A. N., Xiaoying, P. A. N., et al., Two-dimensional Otsu’s curve thresholding segmentation method based on gray and non-local spatial gray feature. Appl. Res. Comput. 5:104, 2012.

    Google Scholar 

  26. Chunying, P., Jikui, L., and Lixi, H., White blood cells image classification based on improving the connection of FCM and LFP. J. Imag. Graph. 18(5):545–551, 2013.

    Google Scholar 

  27. Wang, H., Guo, X. H., Jia, Z. W., et al., Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. Eur. J. Radiol. 74(1):124–129, 2010.

    Article  PubMed  Google Scholar 

  28. Ye, X., Lin, X., Dehmeshki, J., et al., Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans. Biomed. Eng. 56(7):1810–1820, 2009.

    Article  PubMed  Google Scholar 

  29. Gonzales, R. C., Woods, R. E., and Eddins, S. L., Digital image processing using MATLAB. Pearson Prentice Hall, New Jersey, 2004.

    Google Scholar 

  30. Lee, S. L. A., Kouzani, A. Z., and Hu, E. J., Random forest based lung nodule classification aided by clustering. Comput. Med. Imaging Graph. 34(7):535–542, 2010.

    Article  CAS  PubMed  Google Scholar 

  31. Saien, S., Pilevar, A. H., and Moghaddam, H. A., Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels. Comput. Biol. Med. 54:188–198, 2014.

    Article  PubMed  Google Scholar 

  32. Lu, L., Tan, Y., Schwartz, L. H., et al., Hybrid detection of lung nodules on CT scan images. Med. Phys. 42(9):5042–5054, 2015.

    Article  PubMed  Google Scholar 

  33. Guo, W., and Li, Q., High performance lung nodule detection schemes in CT using local and global information. Med. Phys. 39(8):5157–5168, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Choi, W. J., and Choi, T. S., Automated pulmonary nodule detection system in computed tomography images: a hierarchical block classification approach. Entropy 15(2):507–523, 2013.

    Article  Google Scholar 

  35. Netto, S. M. B., Silva, A. C., Nunes, R. A., et al., Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput. Biol. Med. 42(11):1110–1121, 2012.

    Article  Google Scholar 

  36. Choi, W. J., and Choi, T. S., Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput. Meth. Programs Biomed. 113(1):37–54, 2014.

    Article  Google Scholar 

  37. He, C., Fan, X., and Li, Y., Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans. Biomed. Eng. 60(1):230–234, 2013.

    Article  PubMed  Google Scholar 

  38. Wang, D., He, D., Wang, P., et al., Anonymous two-factor authentication in distributed systems: certain goals are beyond attainment. IEEE Trans. Dependable Secur. Comput. 12(4):428–442, 2015.

    Article  Google Scholar 

  39. Wang, D., Wang, N., Wang, P., et al., Preserving privacy for free: efficient and provably secure two-factor authentication scheme with user anonymity. Inf. Sci. 321:162–178, 2015.

    Article  Google Scholar 

  40. He, C. G., Bao, S. D., and Li, Y., A novel tri-factor mutual authentication with biometrics for wireless body sensor networks in healthcare applications. Int. J. Smart Sens. Intell. Syst. 6(3):910–931, 2013.

    Google Scholar 

  41. He, C. G., Cao, C. Z, Bao S. D. An enhanced role-based access control mechanism for hospital information systems. Int. Conf. Comput. Intell. 1001-1005, 2011

Download references

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).

Author information

Author notes

    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

    Authors
    1. Ji-kui Liu
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Hong-yang Jiang
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Meng-di Gao
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Chen-guang He
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Yu Wang
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Pu Wang
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. He Ma
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Ye li
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding authors

    Correspondence to He Ma or Ye li.

    Additional information

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

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

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    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

    Download citation

    • Received:

    • Accepted:

    • Published:

    • DOI: https://doi.org/10.1007/s10916-016-0669-0

    Keywords

    Search

    Navigation