Skip to main content
Springer Nature Link
Log in

Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Initial candidate segmentation is an important task in lung nodule detection. If an accurate segmentation is used, the false positives (FPs) can be decreased in subsequent stages and the desired region of each candidate can be distinguished. In this paper, a new hybrid method for nodule candidate segmentation and FPs reduction is proposed. First, the images are transferred to the neutrosophic domain. Then, three filters, named blob-like structure enhancement (BSE), line structure enhancement (LSE), and central adaptive medialness (CAM) filters, are used for filtering the output of the last step. Afterward, the outputs of BSE, LSE, and CAM filters are used for initial candidate detection and candidate segmentation, respectively. Also, line tracking method is proposed for extending the candidate voxels, and then, it is used for candidate segmentation. After feature extraction, the sparse coding is used for learning feature vector. In the last step, the generalized linear regression model (GLRM) is used for classification. The output of classifier for sensitivity and FP/scan is 98.32% and 2.8, respectively. AUC values of different link functions in GLRM, before and after feature learning, were also calculated, and the best value of AUC was obtained with probit link function after using the sparse coding method. The experimental results demonstrate the power of the proposed algorithm in nodules detection and false positive reduction.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Siegel, R., Jemal, A.: Cancer Facts and Figures 2017. American Cancer Society, Atlanta (2017)

    Google Scholar 

  2. Han, H., Li, L., Han, F., Song, B., Moore, W., Liang, Z.: Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE J. Biomed. Health Inform. 19(2), 648–659 (2015)

    Article  Google Scholar 

  3. Taghavi Namin, S., Abrishami Moghaddam, H., Jafari, R., Esmaeil-Zadeh, M., Gity, M.: Automated detection and classification of pulmonary nodules in 3D thoracic CT images. In: 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), 2010, pp. 3774–3779. IEEE

  4. Riccardi, A., Petkov, T.S., Ferri, G., Masotti, M., Campanini, R.: Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med. Phys. 38(4), 1962–1971 (2011)

    Article  Google Scholar 

  5. Sato, Y., Westin, C.-F., Bhalerao, A., Nakajima, S., Shiraga, N., Tamura, S., Kikinis, R.: Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans. Vis. Comput. Graph. 6(2), 160–180 (2000)

    Article  Google Scholar 

  6. Chen, B., Kitasaka, T., Honma, H., Takabatake, H., Mori, M., Natori, H., Mori, K.: Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images. Int. J. Comput. Assist. Radiol. Surg. 7(3), 465–482 (2012)

    Article  Google Scholar 

  7. Akbarizadeh, G., Moghaddam, A.E.: Detection of lung nodules in CT scans based on unsupervised feature learning and fuzzy inference. J. Med. Imaging Health Inform. 6(2), 477–483 (2016)

    Article  Google Scholar 

  8. Teramoto, A., 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  Google Scholar 

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

    Article  Google Scholar 

  10. Taşcı, E., Uğur, A.: Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs. J. Med. Syst. 39(5), 46 (2015)

    Article  Google Scholar 

  11. Huidrom, R., Chanu, Y.J., Singh, K.M.: Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme. Signal Image Video Process. 1–8 (2018). https://doi.org/10.1007/s11760-018-1327-4

  12. Liu, J., Jiang, H., Gao, M., He, C., Wang, Y.: An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images. J. Med. Syst. 41(2), 30 (2017)

    Article  Google Scholar 

  13. Zhang, M., Zhang, L., Cheng, H.: A neutrosophic approach to image segmentation based on watershed method. Sig. Process. 90(5), 1510–1517 (2010)

    Article  MATH  Google Scholar 

  14. Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Comput. Med. Imaging Graph. 34(3), 213–227 (2010)

    Article  Google Scholar 

  15. Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit. 46(3), 703–715 (2013)

    Article  Google Scholar 

  16. Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)

    Article  Google Scholar 

  17. Guo, Y., Zhou, C., Chan, H.P., Chughtai, A., Wei, J., Hadjiiski, L.M., Kazerooni, E.A.: Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Med. Phy. 40(8), 1–11 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Tahmasbi, A., Saki, F., Aghapanah, H., Shokouhi, S.B.: A novel breast mass diagnosis system based on Zernike moments as shape and density descriptors. In: 2011 18th Iranian Conference of Biomedical Engineering (ICBME), 2011, pp. 100–104. IEEE

  20. Bhattacharya, S., Nurmi, P., Hammerla, N., Plötz, T.: Using unlabeled data in a sparse-coding framework for human activity recognition. Pervasive Mob. Comput. 15, 242–262 (2014)

    Article  Google Scholar 

  21. Akbarizadeh, G., Rahmani, M.: Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images. IET Comput. Vis. 9(5), 629–638 (2015)

    Article  Google Scholar 

  22. Nelder, J.A., Baker, R.J.: Generalized Linear Models. Wiley Online Library (1972)

  23. Dobson, A.J., Barnett, A.: An Introduction to Generalized Linear Models. CRC Press, Boca Raton (2008)

    MATH  Google Scholar 

  24. Horeweg, N., van Rosmalen, J., Heuvelmans, M.A., van der Aalst, C.M., Vliegenthart, R., Scholten, E.T., Groen, H.J.: Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol. 15(12), 1332–1341 (2014)

    Article  Google Scholar 

  25. Atkins, M.S., Mackiewich, B.T.: Fully automatic segmentation of the brain in MRI. IEEE Trans. Med. Imaging 17(1), 98–107 (1998)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Shahid Chamran University of Ahvaz under Grant Number 97/3/02/26247.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Amal Eisapour Moghaddam, Gholamreza Akbarizadeh & Hooman Kaabi

Authors
  1. Amal Eisapour Moghaddam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gholamreza Akbarizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hooman Kaabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gholamreza Akbarizadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eisapour Moghaddam, A., Akbarizadeh, G. & Kaabi, H. Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model. SIViP 13, 457–464 (2019). https://doi.org/10.1007/s11760-018-01413-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-01413-0

Keywords

Search

Navigation