Skip to main content

Towards an Improved Unsupervised Graph-Based MRI Brain Segmentation Method

  • Conference paper
  • First Online:
Cooperative Information Systems (CoopIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14353))

Included in the following conference series:

Abstract

Brain disorders are becoming more prevalent, and accurate brain segmentation is a vital component of identifying the appropriate treatment. This study introduces an enhanced graph-based image segmentation technique. The node selection process involves creating an ellipsoid centered at the image’s center of mass. The proposed approach is evaluated using the NFBS dataset and demonstrates superior visual and numerical outcomes compared to some of existing approaches.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

Similar content being viewed by others

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012). https://doi.org/10.1109/TPAMI.2012.120

    Article  Google Scholar 

  2. Mayala, S., et al.: GUBS: graph-based unsupervised brain segmentation in MRI images. J. Imaging 8(10) (2022). https://doi.org/10.3390/jimaging8100262

  3. Popa, M.: An 3D MRI unsupervised graph-based skull stripping algorithm. Procedia Computer Science, KES 2023, Accepted (2023)

    Google Scholar 

  4. Puccio, B., Pooley, J.P., Pellman, J.S., Taverna, E.C., Craddock, R.C.: The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data. GigaScience 5(1), s13742–016-0150-5 (2016). https://doi.org/10.1186/s13742-016-0150-5

  5. Sadananthan, S.A., Zheng, W., Chee, M.W., Zagorodnov, V.: Skull stripping using graph cuts. Neuroimage 49(1), 225–239 (2010). https://doi.org/10.1016/j.neuroimage.2009.08.050

    Article  Google Scholar 

  6. Saueressig, C., Berkley, A., Kang, E., Munbodh, R., Singh, R.: Exploring graph-based neural networks for automatic brain tumor segmentation. In: Bowles, J., Broccia, G., Nanni, M. (eds.) DataMod 2020. LNCS, vol. 12611, pp. 18–37. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70650-0_2

    Chapter  Google Scholar 

  7. Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)

    Article  Google Scholar 

  8. Taha, A.A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15 (2015)

    Google Scholar 

  9. Wang, L., Zeng, Z., Zwiggelaar, R.: An improved bet method for brain segmentation. In: 2014 22nd International Conference on Pattern Recognition, pp. 3221–3226 (2014). https://doi.org/10.1109/ICPR.2014.555

  10. Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008). https://doi.org/10.1016/j.cviu.2007.08.003

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Popa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Popa, M., Andreica, A. (2024). Towards an Improved Unsupervised Graph-Based MRI Brain Segmentation Method. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-46846-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46845-2

  • Online ISBN: 978-3-031-46846-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics