Skip to main content
SpringerLink
Log in

Fusion-Based Noisy Image Segmentation Method

  • Chapter
  • First Online:
Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 396))

Abstract

A modified algorithm for segmenting microtomography images is given in this work. The main use of the approach is in visualizing structures and calculating statistical object values. The algorithm uses localized edges to initialise snakes for each object separately then moves curves within the images with the help of gradient vector flow (GVF). This leads to object boundary detection and obtain fully segmented complicated images with the aid of methods like region merging and multilevel thresholding.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Radon, J.: Uber die Bestimmung von Funktionen durch ihre Integralwerte Langs Gewisser Mannigfaltigkeiten. Ber. Saechsische Akad. Wiss. 29, 262 (1917)

    MATH  Google Scholar 

  2. Buczkowski, M., Saeed, K.: A multistage approach for noisy micro-tomography images. In: ACSS 2015—2nd International Doctoral Symposium on Applied Computation and Security Systems organized by University of Calcutta (2015)

    Google Scholar 

  3. Paris, S., Kornprobst, P., Tumblin, J., Durand, F.: Bilateral filtering: theory and applications. Found. Trends Comput. Graph. Vis. 4(1), 1–73 (2008)

    Google Scholar 

  4. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  5. Deriche, R.: Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vis. 1(2), 167–187 (1987)

    Article  Google Scholar 

  6. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  7. Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)

    Article  Google Scholar 

  8. 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 

  9. Chenyang, X., Pham, D.L., Prince, J.L.: Image segmentation using deformable models. Handbook Med. Imaging 2, 129–174 (2000)

    Google Scholar 

  10. He, L., et al.: A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 26(2), 141–163 (2008)

    Article  Google Scholar 

  11. Rogowska, J.: Overview and fundamentals of medical image segmentation. In: Handbook of Medical Imaging, pp. 69–85. Academic Press Inc. (2000)

    Google Scholar 

  12. Jahne, B.: Digital Image Processing: Concept, Algorithms, and Scientific Applications. Springer, New York (1997)

    Google Scholar 

  13. Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–165 (2004)

    Article  Google Scholar 

  14. Buczkowski, M., Saeed, K.: A multistep approach for micro tomography obtained medical image. J. Med. Inf. Technol. 23/2014 (2014). ISSN 1642-6037

    Google Scholar 

  15. Buczkowski, M., Saeed, K., Tarasiuk, J., Wroński, S., Kosior, J.: An approach for micro-tomography obtained medical image segmentation. In: Chaki, R., et al. (eds.) Applied Computation and Security Systems, Advances in Intelligent Systems and Computing, vol. 304 (2015)

    Google Scholar 

Download references

Acknowledgements

The research was partially supported by doctoral scholarship IUVENES—KNOW, AGH University of Science and Technology in Krakow and by The Rector of Bialystok University of Technology in Bialystok, grant number S/WI/1/2013.

Author information

Authors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland

    Mateusz Buczkowski

  2. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

Authors
  1. Mateusz Buczkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khalid Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateusz Buczkowski .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Computer Science, DAIS—Università Ca’ Foscari, Venice, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland

    Khalid Saeed

  4. Computer Science & Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this chapter

Cite this chapter

Buczkowski, M., Saeed, K. (2016). Fusion-Based Noisy Image Segmentation Method. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 396. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2653-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2653-6_2

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2651-2

  • Online ISBN: 978-81-322-2653-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation