Skip to main content
SpringerLink
Log in

Computer Assisted Unsupervised Extraction and Validation Technique for Brain Images from MRI

  • Conference paper
  • First Online:
Data Science and Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 462))

  • 309 Accesses

Abstract

Magnetic Resonance Imaging (MRI) of human is a developing field in medical research because it assists in considering the brain anomalies. To identify and analyze brain anomalies, the research requires brain extraction. Brain extraction is a significant clinical image handling method for quick conclusion with clinical perception for quantitative assessment. Automated methods of extracting brain from MRI are challenging, due to the connected pixel intensity information for various regions such as skull, sub head and neck tissues. This paper presents a fully automated extraction of brain area from MRI. The steps involved in developing the method to extract brain area, includes image contrast limited using histogram, background suppression using average filtering, pixel region growing method by finding pixel intensity similarity and filling discontinuity inside brain region. Twenty volumes of brain slices are utilized in this research method. The outcome is achieved by this method is approved by comparing with manually extracted slices. The test results confirm the performance of this strategy can effectively section brain from MRI.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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. Dhawan AP (2003) Medical image analysis. John Wiley Publications and IEEE Press

    Google Scholar 

  2. Barkovich AJ, Millen KJ, Dobyns WB (2009) A developmental and genetic classification for midbrain-hindbrain malformations. Brain-A journal of Neurology. 132:3199–3230

    Article  Google Scholar 

  3. McIlwain H, Bacherlard HS (1985) Biochemistry and the Central nervous system, 5th edn. Churchill Livingstone, Edinburgh

    Google Scholar 

  4. Bankman IN (2000) Handbook of medical imaging, processing and analysis. Academic Press, London

    Google Scholar 

  5. Gayathri SP, Siva Shankar R, Somasundaram (2020) Fetal brain segmentation using improved maximum entropy threshold. Int J Innovative Technology and Exploring Engineering 9:1805–1812

    Google Scholar 

  6. Somasundaram K, Gayathri SP, Rajeswaran R, Dighe M (2018) Fetal brain extraction from magnetic resonance iumage (MRI) of human fetus. The Imaging Science J 66:133–138

    Article  Google Scholar 

  7. Hwang J, Han Y, Park H (2011) Skull-stripping method for brain MRI using a 3D level Set with a speedup operator. J Magn Reson Imaging 34:445–456

    Article  Google Scholar 

  8. Genish T, Prathapchandran K, Gayathri SP (2019) An approach to segment the Hippocampusfrom T2-weighted MRI of human head scans for the diagnosis of Alzheimer’s disease using Fuzzy C-means clustering. Adv Algebra Analysis. 1:333–342

    Google Scholar 

  9. Smith SM (2002) Fast robust automated brain extraction (BET). Hum Brain Mapp 17:143–155

    Article  Google Scholar 

  10. Zu YS, Guang HY, Jing ZL (2002) Automated histogram—based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. Neuroimage 17:1587–1598

    Article  Google Scholar 

  11. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Transaction on Systems, Man, and Cybernetics. 9:62–66

    Article  Google Scholar 

  12. Jaccard P (1912) The distribution of Flora in Alpine Zone. New Phytol 11:37–50

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Altman DG, Bland JM (1994) Statistics notes: diagnostic tests 1: sensitivity and specificity. BMJ 308:1552

    Article  Google Scholar 

  15. International Brain Segmentation Repository, Center for Morphometric Analysis Massachusetts General Hospital, CNY-6, Building 149, 13th Street, Charlestown, MA, 02129-USA.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Data Science, Christ—Deemed to be University, Pune, India

    S. Vijayalakshmi

  2. School of Computing Science, KPR College of Arts Science and Research, Coimbatore, India

    T. Genish

  3. Department of Computer Science and Applications, The Gandhigram Rural Institute–Deemed to be University, Gandhigram, India

    S. P. Gayathri

Authors
  1. S. Vijayalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Genish
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. P. Gayathri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Genish .

Editor information

Editors and Affiliations

  1. Christ University, Bengaluru, Karnataka, India

    Samiksha Shukla

  2. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  3. Christ University, Bengaluru, Karnataka, India

    Joseph Varghese Kureethara

  4. Department of Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Mishra

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Vijayalakshmi, S., Genish, T., Gayathri, S.P. (2022). Computer Assisted Unsupervised Extraction and Validation Technique for Brain Images from MRI. In: Shukla, S., Gao, XZ., Kureethara, J.V., Mishra, D. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 462. Springer, Singapore. https://doi.org/10.1007/978-981-19-2211-4_32

Download citation

Publish with us

Policies and ethics

Search

Navigation