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International Conference on Advanced Informatics for Computing Research

ICAICR 2019: Advanced Informatics for Computing Research pp 451–463Cite as

Analysis of Graph Cut Technique for Medical Image Segmentation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1075))

Abstract

Segmentation plays an important role in image analysis as it is used to identify and differentiate foreground and background regions. Image segmentation in brain MRI analysis performs several roles like extraction of abnormal region for better diagnosis of the disease aiding in the therapy planning. Various brain tumors comprise diverse properties like their shapes, intensity distribution and location, hence reducing the possibility of developing a single general algorithm. In this paper authors have illustrated two methods for performing extraction which includes histogram thresholding and centroid based graph cut segmentation. On the basis of their potential, advantages and limitation comparison is made, that emphasize better performance of centroid based graph cut segmentation method. To measure the performance some quality parameters are evaluated. This paper also solves the problem of initial seed selection by using graph cut segmentation technique.

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References

  1. Kotsas, P.: Non-rigid registration of medical images using an automated method, pp. 199–201. IEC, Prague (2005)

    Google Scholar 

  2. Nagalkar, V., Asole, S.: Brain tumor detection using digital image processing based on soft computing. J. Signal Image Process. 3, 102–105 (2012)

    Google Scholar 

  3. Mirajkar, G., Barbadekar, B.: Automatic segmentation of brain tumors from MR images using undecimated wavelet transform and gabor wavelets. In: 17th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), pp. 702–705 (2010)

    Google Scholar 

  4. Lin, C.-T., Yeh, C.-M., Liang, S.-F., Chung, J.-F., Kumar, N.: Support-vector-based fuzzy neural network for pattern classification. IEEE Trans. Fuzzy Syst. 14, 31–41 (2006)

    Article  Google Scholar 

  5. Cheriet, M., Said, J.N., Suen, C.Y.: A recursive thresholding technique for image segmentation. IEEE Trans. Image Process. 7, 918–921 (1998)

    Article  Google Scholar 

  6. Sezgin, M., Sankur, B.: Selection of thresholding methods for nondestructive testing applications. In: Proceedings of International Conference on Image Processing, pp. 764–767 (2001)

    Google Scholar 

  7. Li, Z., Liu, G., Zhang, D., Xu, Y.: Robust single-object image segmentation based on salient transition region. Pattern Recogn. 52, 317–331 (2016)

    Article  Google Scholar 

  8. Manousakas, I., Undrill, P., Cameron, G., Redpath, T.: Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. Comput. Biomed. Res. 31, 393–412 (1998)

    Article  Google Scholar 

  9. Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16, 641–647 (1994)

    Article  Google Scholar 

  10. Fan, J., Yau, D.K., Elmagarmid, A.K., Aref, W.G.: Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans. Image Process. 10, 1454–1466 (2001)

    Article  Google Scholar 

  11. Hancer, E., Karaboga, D.: A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm Evol. Comput. 32, 49–67 (2017)

    Article  Google Scholar 

  12. Gambotto, J.-P.: A new approach to combining region growing and edge detection. Pattern Recogn. Lett. 14, 869–875 (1993)

    Article  Google Scholar 

  13. Pavlidis, T., Liow, Y.-T.: Integrating region growing and edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 12, 225–233 (1990)

    Article  Google Scholar 

  14. Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26, 1277–1294 (1993)

    Article  Google Scholar 

  15. Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651–666 (2010)

    Article  Google Scholar 

  16. Dhanachandra, N., Chanu, Y.J.: Image segmentation method using k-means clustering algorithm for color image. Adv. Res. Electr. Electron. Eng. 2(11), 68–72 (2015)

    Google Scholar 

  17. Despotovic, I., Vansteenkiste, E., Philips, W.: Spatially coherent fuzzy clustering for accurate and noise-robust image segmentation. IEEE Signal Process. Lett. 20, 295–298 (2013)

    Article  Google Scholar 

  18. Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30, 9–15 (2006)

    Article  Google Scholar 

  19. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient ND image segmentation. Int. J. Comput. Vis. 70, 109–131 (2006)

    Article  Google Scholar 

  20. Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1101–1113 (1993)

    Article  Google Scholar 

  21. Ford Jr., L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (2015)

    MATH  Google Scholar 

  22. Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. J. ACM (JACM) 35, 921–940 (1988)

    Article  MathSciNet  Google Scholar 

  23. Boykov, Y., Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: null, p. 26 (2003)

    Google Scholar 

  24. Heimowitz, A., Keller, Y.: Image segmentation via probabilistic graph matching. IEEE Trans. Image Process. 25, 4743–4752 (2016)

    Article  MathSciNet  Google Scholar 

  25. Mortensen, E.N., Barrett, W.A.: Interactive segmentation with intelligent scissors. Graph. Models Image Process. 60, 349–384 (1998)

    Article  Google Scholar 

  26. Falcão, A.X., Udupa, J.K., Miyazawa, F.K.: An ultra-fast user-steered image segmentation paradigm: live wire on the fly. IEEE Trans. Med. Imaging 19, 55–62 (2000)

    Article  Google Scholar 

  27. Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: Proceedings of Eighth IEEE International Conference on Computer Vision, pp. 105–112 (2001)

    Google Scholar 

  28. Stubberfield, L.: Big Picture. https://bigpictureeducation.com

  29. MathWorks. https://in.mathworks.com/matlabcentral.com

  30. 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, 600–612 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication, Jaypee University of Information Technology, Solan, India

    Jyotsna Dogra, Shruti Jain & Meenakshi Sood

Authors
  1. Jyotsna Dogra
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  2. Shruti Jain
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  3. Meenakshi Sood
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Corresponding author

Correspondence to Jyotsna Dogra .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Computer Science Department, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

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

    Xiao-Zhi Gao

  5. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada

    Pawan Lingras

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Dogra, J., Jain, S., Sood, M. (2019). Analysis of Graph Cut Technique for Medical Image Segmentation. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_42

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  • DOI: https://doi.org/10.1007/978-981-15-0108-1_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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