Skip to main content
SpringerLink
Log in

A Novel Computer-Aided Diagnosis Method of Nasopharyngeal Carcinoma Based on Magnetic Resonance Images

  • Conference paper
  • First Online:
Internet Multimedia Computing and Service (ICIMCS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 819))

Included in the following conference series:

  • 1370 Accesses

Abstract

A novel computer-aided method based on magnetic resonance images (MRI) was proposed for the early detection and diagnosis of nasopharyngeal carcinoma (NPC). A local Chan-Vese level-set model, which integrated the maximum interclass-variance method with the Chan-Vese model, was built to detect foci with unobvious boundaries. For each of the suspected foci, 26 features, including suspected focus texture, shape, and grayscale characteristics, were extracted, and then classified with a support-vector-machine (SVM) classifier. The method was tested with 289 brain images of 48 patients with nasopharyngeal carcinoma and 33 healthy adults, which obtained an average successful-diagnosis rate of 90.74%.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 107.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Prasad, U., Pathmanathan, R., Sam, C.K., Rampal, L., Singh, J.: Early diagnosis of nasopharyngeal carcinoma: a multi-pronged approach. In: Ablashi, D.V., Faggioni, A., Krueger, G.R.F., Pagano, J.S., Pearson, G.R. (eds.) Epstein-Barr Virus and Human Disease Experimental Biology and Medicine. EBAM, vol. 20. Humana Press, New York (1989). https://doi.org/10.1007/978-1-4612-4508-7_58

    Google Scholar 

  2. Liu, D.J., Yuan, L.I., Wei-Min, A.N., et al.: Study on liver cancer computer-aided diagnosis based on medical imaging. Chin. Med. Equip. (2015)

    Google Scholar 

  3. Wang, X.F., Nie, S.D., Wang, Y.J.: Progress in computer-aided detection for brain tumor using MRI. Chin. J. Med. Phys. 1, 11 (2014)

    Google Scholar 

  4. Chanapai, W., Bhongmakapat, T., Tuntiyatorn, L., et al.: Nasopharyngeal carcinoma segmentation using a region growing technique. Int. J. Comput. Assist. Radiol. Surg. 7, 413–422 (2012)

    Article  Google Scholar 

  5. Ritthipravat, P., Tatanun, C., Bhongmakapat, T., et al.: Automatic segmentation of nasopharyngeal carcinoma from CT images. In: International Conference on BioMedical Engineering and Informatics, vol. 2, pp. 18–22 (2008)

    Google Scholar 

  6. Fitton, I., Cornelissen, S.A.P., Duppen, J.C., et al.: Semi-automatic delineation using weighted CT-MRI registered images for radiotherapy of nasopharyngeal cancer. Med. Phys. 38, 4662–4666 (2011)

    Article  Google Scholar 

  7. Han, D., Bayouth, J., Song, Q., Taurani, A., Sonka, M., Buatti, J., Wu, X.: Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 245–256. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22092-0_21

    Chapter  Google Scholar 

  8. Dvořák, P., Kropatsch, W.G., Bartušek, K.: Automatic brain tumor detection in T2-weighted magnetic resonance images. Meas. Sci. Rev. 13(5), 223–230 (2013)

    Google Scholar 

  9. Ghanavati, S., Li, J., Liu, T., et al.: Automatic brain tumor detection in magnetic resonance images. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 574–577 (2012)

    Google Scholar 

  10. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  11. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  12. Wang, X.F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010)

    Article  MATH  Google Scholar 

  13. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  14. Zacharaki, E.I., Wang, S., Chawla, S., et al.: Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med. 62(6), 1609–1618 (2009)

    Article  Google Scholar 

  15. Saha, M., Mukherjee, R., Chakraborty, C.: Computer-aided diagnosis of breast cancer using cytological images: a systematic review. Tissue Cell 48(5), 461–474 (2016)

    Article  Google Scholar 

  16. Jayachandran, A., Dhanasekaran, R.: Brain tumor detection and classification of MR images using texture features and fuzzy SVM classifier. Res. J. Appl. Sci. Eng. Technol. 6(12), 2264–2269 (2013)

    Google Scholar 

Download references

Acknowledgments

This work is supported in part by the Guangzhou Key Lab of Body Data Science (201605030011) and the Diabetes Intelligent Wear Monitoring Equipment and Complications Prevention and Control Cloud Platform (2016B010108008) and the Research and Application of Mobile Medical Technology (2015B010106008).

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, Guangdong, China

    Xiang Tian, Yan Zhang, Chunmei Qing & Lin Shu

  2. Information Department, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China

    Qingbin Wu

  3. Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China

    Changzheng Shi & Xiaoping Li

Authors
  1. Xiang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingbin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changzheng Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chunmei Qing
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lin Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingbin Wu .

Editor information

Editors and Affiliations

  1. Multimedia Communications Department, EURECOM, Sophia Antipolis, France

    Benoit Huet

  2. Shandong University , Qingdao, China

    Liqiang Nie

  3. Hefei University of Technology , Hefei, China

    Richang Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tian, X. et al. (2018). A Novel Computer-Aided Diagnosis Method of Nasopharyngeal Carcinoma Based on Magnetic Resonance Images. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8530-7_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8529-1

  • Online ISBN: 978-981-10-8530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation