Skip to main content
SpringerLink
Log in

Internal Generative Mechanism Based Otsu Multilevel Thresholding Segmentation for Medical Brain Images

  • Conference paper
  • First Online:
Advances in Multimedia Information Processing -- PCM 2015 (PCM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9314))

Included in the following conference series:

Abstract

Recent brain theories indicate that perceiving an image visually is an active inference procedure of the brain by using the Internal Generative Mechanism (IGM). Inspired by the theory, an IGM based Otsu multilevel thresholding algorithm for medical images is proposed in this paper, in which the Otsu thresholding technique is implemented on both the original image and the predicted version obtained by simulating the IGM on the original image. A regrouping measure is designed to refining the segmentation result. The proposed method takes the predicted visual information generated by the complicated Human Visual System (HVS) into account, as well as the details. Experiments on medical MR-T2 brain images are conducted to demonstrate the effectiveness of the proposed method. The experimental results indicate that the IGM based Otsu multilevel thresholding is superior to the other multilevel thresholdings.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Läthén, G. Segmentation Methods for Medical Image Analysis (2010)

    Google Scholar 

  2. Farmer, M.E., Jain, A.K.: A wrapper-based approach to image segmentation and classification. IEEE Trans. Image Process. 14(12), 2060–2072 (2005)

    Article  Google Scholar 

  3. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)

    Article  Google Scholar 

  4. Sun, C., Lu, H., Zhang, W., Qiu, X., Li, F., Zhang, H.: Lip segmentation based on facial complexion template. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 193–202. Springer, Heidelberg (2014)

    Google Scholar 

  5. Peng, B., Zhang, D.: Automatic image segmentation by dynamic region merging. IEEE Trans. Image Process. 20(12), 3592–3605 (2011)

    Article  MathSciNet  Google Scholar 

  6. Maulik, U.: Medical image segmentation using genetic algorithms. IEEE Trans. Inf Technol. Biomed. 13(2), 166–173 (2009)

    Article  Google Scholar 

  7. Manikandan, S., Ramar, K., Iruthayarajan, M.W., et al.: Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm. Measurement 47, 558–568 (2014)

    Article  Google Scholar 

  8. Sathya, P.D., Kayalvizhi, R.: Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Neurocomputing 74(14), 2299–2313 (2011)

    Article  Google Scholar 

  9. Maitra, M., Chatterjee, A.: A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging. Measurement 41(10), 1124–1134 (2008)

    Article  Google Scholar 

  10. Sezgin, M.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)

    Article  Google Scholar 

  11. Liao, P.S., Chen, T.S., Chung, P.C.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713–727 (2001)

    Google Scholar 

  12. Huang, D.Y., Wang, C.H.: Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recogn. Lett. 30(3), 275–284 (2009)

    Article  Google Scholar 

  13. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)

    Google Scholar 

  14. Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recogn. 19(1), 41–47 (1986)

    Article  Google Scholar 

  15. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273–285 (1985)

    Article  Google Scholar 

  16. Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst. Appl. 38(5), 4998–5004 (2011)

    Article  Google Scholar 

  17. Sternberg, R.: Cognitive Psychology. Cengage Learning, Belmont (2011)

    Google Scholar 

  18. Zhai, G., Wu, X., Yang, X., et al.: A psychovisual quality metric in free-energy principle. IEEE Trans. Image Process. 21(1), 41–52 (2012)

    Article  MathSciNet  Google Scholar 

  19. Kersten, D., Mamassian, P., Yuille, A.: Object perception as Bayesian inference. Annu. Rev. Psychol. 55, 271–304 (2004)

    Article  Google Scholar 

  20. Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–138 (2010)

    Article  Google Scholar 

  21. Zhang, X., Li, X., Feng, Y., et al.: Image fusion with internal generative mechanism. Expert Syst. Appl. 42(5), 2382–2391 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China for Youths (No. 61305046), Jilin Province Science Foundation for Youths (No. 20130522117JH), and the Natural Science Foundation of Jilin Province (No. 20140101193JC).

Author information

Authors and Affiliations

  1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Yuncong Feng, Xuanjing Shen, Haipeng Chen & Xiaoli Zhang

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Yuncong Feng, Xuanjing Shen, Haipeng Chen & Xiaoli Zhang

Authors
  1. Yuncong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuanjing Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haipeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoli Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haipeng Chen .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. ICU, IVY Lab, KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Feng, Y., Shen, X., Chen, H., Zhang, X. (2015). Internal Generative Mechanism Based Otsu Multilevel Thresholding Segmentation for Medical Brain Images. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24075-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24074-9

  • Online ISBN: 978-3-319-24075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation