Automatic Brain Tumor Segmentation on Preoperative and Postoperative MRI Using Region Growing Algorithm

Authors

  • K.V. Shiny  Research Scholar, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India
  • N. Sugitha  Associate Professor, Department of Information Technology, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

Keywords:

Brain tumor segmentation, MR Image, region growing, necrotic tissue segmentation, enhancing cell, radio surgery, radiotherapy.

Abstract

Detection and segmentation of mixed necrotic and tumor tissue along with the neighboring vessels is a challenging scenario in radiation oncology application. The MRI image is an image that produces a high contrast images indicating regular and irregular tissues that help to distinguish the overlapping in margin of each tissue. All automatic seed finding methodologies may suffer with the problem if there is no growth of tumor and if any small white part or grey part is present there. Segmentation of images with complex structures such as magnetic resonance brain images is difficult using general purpose methods. Region based active contour models are widely used in brain tumor segmentation. But when the edges of tumor is not sharpen, then the segmentation results are not accurate i.e. segmentation may be over or under that may happened due to initial stage of the tumors. Here a method of tumor detection based on texture of the MRI and if it is detected then to segment it automatically using automatic seeded region growing method is proposed in to separate the irregular from the regular surrounding tissue to get a real identification of involved and non-involved area that help the surgeon to distinguish the affected area precisely. The methods used in this paper are texture analysis and automatic seeded region growing method and is implemented on MRI of brain to detect the tumor boundaries in 2D MRI for different cases.

References

  1. AndacHamamci*, Nadir Kucuk, KutlayKaraman, KayihanEngin, and GozdeUnal, Senior Member, IEEE, “Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications,” IEEETran. MedImag., Vol. 31, No. 3, March 2012. [1]
  2. S. Warfield, K. Zou, and W. Wells, “Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation,” IEEE Trans. Med Imag., vol. 23, no. 7, pp. 903–921, Jul. 2004.
  3. K. H. Zou, S. K.Warfield,A.Bharatha, C. M. C. Tempany, M. R. Kaus,S. J. Haker,W.M.Wells, F. A. Jolesz, and R. Kikinis, “Statistical validationof image segmentation quality based on a spatial overlap index,”Acad. Radiol., vol. 11, no. 2, pp. 178–189, 2004.
  4. E. D. Angelini, O. Clatz, E. Mandonnet, E. Konukoglu, L. Capelle, and H. Duffau, “Glioma dynamics and computational models: A reviewof segmentation, registration, and in silico growth algorithms andtheir clinical applications,” Curr. Med. Imag. Rev., vol. 3, no. 4, pp.262–276, 2007.
  5. M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, “A brain tumor segmentationframework based on outlier detection,” Med. Image Anal., vol.8, no. 3, pp. 275–283, 2004.
  6. J.Liu, J. K. Udupa, D. Odhner,D.Hackney, and G. Moonis, “Asystemfor brain tumor volume estimation viaMR imaging and fuzzy connectedness,”Comput. Med. Imag. Graph., vol. 29, pp. 21–34, 2005.
  7. T. Biswaset al., “Stereotactic radiosurgery for glioblastoma: Retrospectiveanalysis.,” Radiation Oncology, vol. 4, no. 11, p. 11, 2009.
  8. A. Gooya, G. Biros, and C. Davatzikos, “Deformable registration ofglioma images using em algorithm and diffusion reaction modeling,”IEEE Trans. Med. Imag., vol. 30, no. 2, pp. 375–390, Feb. 2011.
  9. B. Menze, K. V. Leemput, D. Lashkari, M.-A.Weber, N. Ayache, andP. Golland, “A generative model for brain tumor segmentation in multimodalimages,” Med. Image Comput. Comput. Assist. Intervent., vol.13, pp. 151–159, Sep. 2010.
  10. T. F. Chan and L. Vese, “Active contours without edges,” IEEE Trans.Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.
  11. S. Ho, E. Bullitt, and G. Gerig, “Level-set evolution with region competition:Automatic 3-D segmentation of brain tumors,” in Proc. ICPR,2002, vol. 1, p. 10532.
  12. Y. Boykov and M.-P.Jolly, “Interactive graph cuts for optimalboundary and region segmentation of objects in n-d images,” in Proc.ICCV, 2001, pp. 105–112.
  13. L. Grady, “Random walks for image segmentation,” in IEEE Trans.Pattern Anal. Mach. Intell., Nov. 2006, vol. 28, no. 11, pp. 1768–1783.
  14. C. Couprie, L. Grady, L. Najman, and H. Talbot, “Power watersheds:A new image segmentation framework extending graph cuts, randomwalker and optimal spanning forest,” in ICCV, 2009, pp. 731–738.
  15. A. Sinop and L. Grady, “A seeded image segmentation framework unifyinggraph cuts and random walker which yields a new algorithm,” inICCV, 2007, pp. 1–8.
  16. A. Criminisi, T. Sharp, and A. Blake, “GeoS: Geodesic image segmentation,”in Comput. Vis. ECCV 2008, 2008, vol. 5302, pp. 99–112.
  17. X. Bai and G. Sapiro, “Geodesic matting: A framework for fast interactiveimage and video segmentation and matting,” Int. J. Comput. Vis.,vol. 82, pp. 113–132, 2009.
  18. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A.Agarwala, M. Tappen, and C. Rother, “A comparative study of energyminimization methods for Markov random fields with smoothness-based priors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30,pp. 1068–1080, 2008.
  19. V. Vezhnevets and V. Konouchine, “Growcut-interactive multi-labeln-d image segmentation by cellular automata,” presented at the Graphicon,NovosibirskAkademgorodok, Russia, 2005.
  20. A. Popovici and D. Popovici, “Cellular automata in image processing,”inProc. 15th Int. Symp. Math. Theory Networks Syst., 2002, pp. 34–44.
  21. J. V. Neumann, Theory of self-reproducing automata, A. W. Burks,Ed. Champaign, IL: Univ. Illinois Press, 1966.
  22. N. Ganguly, B. K. Sikdar, A. Deutsch, G. Canright, and P. P. Chaudhuri,A survey on cellular automata Centre for high performance computing,Dresden Univ. Technol., Dresden, Germany, Tech. Rep., 2003.
  23. J. Kari, “Theory of cellular automata: A survey,” Theoretical Comput.Sci., vol. 334, no. 1–3, pp. 3–33, 2005.
  24. K. Kaneko, “Overview of coupled map lattices,” Chaos, vol. 2, no. 3,pp. 279–282, 1992.
  25. D. N. Ostrov and R. Rucker, “Continuous-valued cellular automata fornonlinear wave equations,” Complex Syst., vol. 10, pp. 91–119, 1996.
  26. S.Wolfram, A New Kind of Science. Champaign, IL:WolframMedia, 2002, ch. 4, p. 155.
  27. A. Hamamci, G. Unal, N. Kucuk, and K. Engin, “Cellular automatasegmentation of brain tumors on post contrast MR images,” inMICCAI. New York: Springer, 2010, pp. 137–146.
  28. C. Kauffmann and N. Pich, “Seeded ND medical image segmentationby cellular automaton on GPU,” Int. J. Comput. Assist. Radiol.Surg.,vol. 5, pp. 251–262, 2010.
  29. G. Hernandez and H. J. Herrmann, “Cellular automata for elementaryimage enhancement,” Graphical Models Image Process., vol. 58, no.1, pp. 82–89, 1996.
  30. Mukesh Kumar and Kamal K.Mehta, “A Texture based Tumor detection and automatic Segmentation using Seeded Region Growing Method”, Int. J. Comp. Tech. Appl., Vol 2 (4), 855-859,2011.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K.V. Shiny, N. Sugitha, " Automatic Brain Tumor Segmentation on Preoperative and Postoperative MRI Using Region Growing Algorithm, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.1068-1076, March-April-2021.