Skip to main content
SpringerLink
Log in

Brain Tumor Segmentation Using Random Walks from MRI Images

  • Conference paper
  • First Online:
Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1299))

Abstract

Magnetic resonance imaging (MRI) is a vital and universally recognized medium to assess brain neoplasms. This paper presents a study on brain tumor segmentation based on the random walk algorithm which is a graph-based method in which pixels of a brain MR image are treated as nodes. Segmentation is performed by interactively labeling certain nodes as foreground and background seeds, followed by computing the probability of each unlabeled node to reach all the labeled nodes using random paths. The method is applied on two different MR modalities viz. T2-weighted MRI with fluid attenuated inversion recovery (FLAIR), and T2 MRI to segment complete tumor, and tumor core regions, respectively, by utilizing visual traits of MRI images and identifying local and global brain tissues information. Efficacy is validated quantitatively as well as qualitatively through performing the experiments on publicly available brain tumor segmentation challenge (BRATS-2013) dataset. Results demonstrate that the proposed method performs favorable as compared to several existing methods.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Notes

  1. 1.

    https://www.smir.ch/BRATS/Start2013.

References

  1. Agn, M., Puonti, O., Law, I., af Rosenschöld, P., van Leemput, K.: Brain tumor segmentation by a generative model with a prior on tumor shape. In: Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 1–4. Springer (2015)

    Google Scholar 

  2. Bauer, S., Nolte, L.P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 354–361. Springer (2011)

    Google Scholar 

  3. Corso, J.J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.: Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans. Medi. Imag. 27(5), 629–640 (2008)

    Article  Google Scholar 

  4. Courant, R., Hilbert, D.: Methods of mathematical physics, vol. 2. Wiley (1989)

    Google Scholar 

  5. Demirhan, A., Törü, M., Güler, I.: Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J. Biomed. Health Inform. 19(4), 1451–1458 (2014)

    Article  Google Scholar 

  6. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  7. Dodziuk, J.: Difference equations, isoperimetric inequality and transience of certain random walks. Trans. Am. Math. Soc. 284(2), 787–794 (1984)

    Article  MathSciNet  Google Scholar 

  8. Drevelegas, A.: Imaging of brain tumors with histological correlations. Springer Science & Business Media (2010)

    Google Scholar 

  9. Geremia, E., Menze, B.H., Ayache, N., et al.: Spatial decision forests for glioma segmentation in multi-channel mr images. In: MICCAI Challenge on Multimodal Brain Tumor Segmentation, vol. 34, pp. 14–18. Citeseer (2012)

    Google Scholar 

  10. Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 1768–1783 (2006)

    Article  Google Scholar 

  11. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, pp. 147–151. Citeseer (1988)

    Google Scholar 

  12. Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  13. Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  14. Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013)

    Article  Google Scholar 

  15. Letteboer, M.M., Olsen, O.F., Dam, E.B., Willems, P.W., Viergever, M.A., Niessen, W.J.: Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm1. Acad. Radiol. 11(10), 1125–1138 (2004)

    Article  Google Scholar 

  16. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imag. 34(10), 1993 (2015)

    Article  Google Scholar 

  17. Menze, B.H., Van Leemput, K., Lashkari, D., Weber, M.A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 151–159. Springer (2010)

    Google Scholar 

  18. Pei, L., Bakas, S., Vossough, A., Reza, S.M., Davatzikos, C., Iftekharuddin, K.M.: Longitudinal brain tumor segmentation prediction in mri using feature and label fusion. Biomed. Signal Process. Control 55, 101,648 (2020)

    Google Scholar 

  19. Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A novel content-based active contour model for brain tumor segmentation. Magn. Resonance Imag. 30(5), 694–715 (2012)

    Article  Google Scholar 

  20. Shivhare, S.N., Kumar, N., Singh, N.: A hybrid of active contour model and convex hull for automated brain tumor segmentation in multimodal mri. In: Multimedia Tools and Applications, pp. 1–23 (2019)

    Google Scholar 

  21. Taylor, T., John, N., Buendia, P., Ryan, M.: Map-reduce enabled hidden Markov models for high throughput multimodal brain tumor segmentation. In: Multimodal Brain Tumor Segmentation, vol. 43 (2013)

    Google Scholar 

  22. Tong, J., Zhao, Y., Zhang, P., Chen, L., Jiang, L.: Mri brain tumor segmentation based on texture features and kernel sparse coding. Biomed. Signal Process. Control 47, 387–392 (2019)

    Article  Google Scholar 

  23. Ullmann, J.F., Janke, A.L., Reutens, D., Watson, C.: Development of mri-based atlases of non-human brains. J. Comp. Neurol. 523(3), 391–405 (2015)

    Article  Google Scholar 

  24. Zhao, J., Meng, Z., Wei, L., Sun, C., Zou, Q., Su, R.: Supervised brain tumor segmentation based on gradient and context-sensitive features. Front. Neurosci. 13 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Uttarakhand, Srinagar Garhwal, Uttarakhand, India

    Shiv Naresh Shivhare & Nitin Kumar

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Shiv Naresh Shivhare

Authors
  1. Shiv Naresh Shivhare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nitin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shiv Naresh Shivhare .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  3. Department of Computer Science and Engineering, S ‘O’ A Deemed to be University, Bhubaneswar, Odisha, India

    Binod Kumar Pattanayak

  4. Faculty of Information and Communication Technology, Université des Mascareignes, Pamplemousses, Mauritius

    Seeven Amic

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shivhare, S.N., Kumar, N. (2021). Brain Tumor Segmentation Using Random Walks from MRI Images. In: Panigrahi, C.R., Pati, B., Pattanayak, B.K., Amic, S., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-33-4299-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-4299-6_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4298-9

  • Online ISBN: 978-981-33-4299-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation