Skip to main content
SpringerLink
Log in

An Experimental Study for Monitoring the Changes in the Brain Stroke Images Using Image Similarity Measures

  • Conference paper
  • First Online:
Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (SoCPaR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 417))

Included in the following conference series:

  • 569 Accesses

Abstract

Image Similarity measures are used in the field of imaging to get the quantitative evaluation of the similarity between two images. These measures can be used to monitor the changes in the brain images which can help in diagnosis and treatment and prediction of brain diseases. The objective of the proposed work is to assess how effectively the image similarity metrics can be used as image similarity measures in finding the abnormalities in the brain stroke images. The proposed work uses six image similarity measures and are applied to brain stroke images for monitoring the changes in the brain stroke images. The performance of the measures shows significant results in assessing the similarity between the brain stroke images.

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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  2. Lashkari, A.: A neural network-based method for brain abnormality detection in MR images using Zernike moments and geometric moments. Int. J. Comput. Appl. 4(7), 1–8 (2010)

    Google Scholar 

  3. Moraru, L.: Hemorrhage detection in MRI brain images using images features. In: AIP Conference Proceedings, vol. 1564, p. 171 (2013). https://doi.org/10.1063/1.4832814

  4. Ahmed, O.B., et al.: Alzheimer’s disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex. Comput. Med. Imaging Graph. 44, 13–25 (2015)

    Article  Google Scholar 

  5. Raju, K.N., Reddy, K.S.P.: Comparative study of structural similarity index (SSIM) by using different edge detection approaches on live video frames for different color models. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 932–937 (2017). https://doi.org/10.1109/ICICICT1.2017.8342690

  6. Sara, U., Akter, M., Uddin, M.: Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. J. Comput. Commun. 7, 8–18 (2019). https://doi.org/10.4236/jcc.2019.73002

    Article  Google Scholar 

  7. Nemcova, A., Smisek, R., Marsanova, L., Smital, L., Vítek, M.: A comparative analysis of methods for evaluation of ECG signal quality after compression. Biomed. Res. Int. 2018, 1868519 (2018). https://doi.org/10.1155/2018/1868519

    Article  Google Scholar 

  8. Razlighi, Q.R., Kehtarnavaz, N., Yousefi, S.: Evaluating similarity measures for brain image registration. J. Vis. Commun. Image Represent. 24(7), 977–987 (2013). https://doi.org/10.1016/j.jvcir.2013.06.010

    Article  Google Scholar 

  9. Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition, pp. 2366–2369 (2010). https://doi.org/10.1109/ICPR.2010.579

  10. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Sig. Process. Lett. 9(3), 81–84 (2002). https://doi.org/10.1109/97.995823

    Article  Google Scholar 

  11. Song, Y., Kube, C.M., Zhang, J., Li, X.: Higher-order spatial correlation coefficients of ultrasonic backscattering signals using partial cross-correlation analysis. J. Acoust. Soc. Am. 147, 757 (2020). https://doi.org/10.1121/10.0000615

    Article  Google Scholar 

  12. Kaur, A., Kaur, L., Gupta, S.: Image recognition using coefficient of correlation and structural SIMilarity index in uncontrolled environment. Int. J. Comput. Appl. (0975 – 8887) 59(5), 32–39 (2012)

    Google Scholar 

  13. Wang, L., Zhang, Y., Feng, J.: On the Euclidean DISTANCE OF images. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1334–1339 (2005). https://doi.org/10.1109/TPAMI.2005.165

    Article  Google Scholar 

  14. Punga, M.: Structural similarity analysis for brain MR image quality assessment. In: AIP Conference Proceedings, vol. 1634, p. 137 (2014). https://doi.org/10.1063/1.4903028

  15. Mamun, I.: Image classification using SSIM, simple image classifier with OpenCV (2019). https://towardsdatascience.com/image-classification-using-ssim-34e549ec6e12

  16. Agrawal, P.: A beginners’ guide to image similarity using python (2021). This article was published as a part of the Data Science Blogathon

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai, India

    R. Maruthi & Anitha S. Pillai

  2. Department of Neurology, Apollo Speciality Hospitals, Nellore, India

    Bindu Menon

Authors
  1. R. Maruthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anitha S. Pillai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bindu Menon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Maruthi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Matieland, South Africa

    Andries Engelbrecht

  3. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Fabio Scotti

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. University of Mumbai, Mumbai, Maharashtra, India

    Pooja Manghirmalani Mishra

  6. University of Calabria (Unical), Rende, Italy

    Giancarlo Fortino

  7. Department of Informatics, Vilnius University, Kaunas, Lithuania

    Virgilijus Sakalauskas

  8. Center for Smart Computing Continuum, Forschung Burgenland, Eisenstadt, Austria

    Sabri Pllana

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Maruthi, R., Pillai, A.S., Menon, B. (2022). An Experimental Study for Monitoring the Changes in the Brain Stroke Images Using Image Similarity Measures. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_46

Download citation

Publish with us

Policies and ethics

Search

Navigation