Skip to main content
Log in

Efficient three-dimensional super-diffusive model for benign brain tumor segmentation

  • Regular Article
  • Published:
The European Physical Journal Plus Aims and scope Submit manuscript

Abstract

Brain is most complex and central part of the human body. Millions of cells are present in the brain. Brain tumor is extra unwanted cell in the human brain. It is mainly categorized into benign and malignant. Benign tumor cells have very similar characteristics with its surrounding cells. Its accurate detection and segmentation is very challenging task. Image segmentation methods have major contribution in detection and segmentation of these tumor cells. Segmentation methods are either boundary based or region based. These methods use traditional integral-order calculus. It has been observed that these approaches are unable to detect low variational region such as benign tumor. In the present manuscript, fractional diffusion-based benign brain tumor detection and segmentation method is being proposed. It has been observed that the proposed method is able to detect and segment benign brain tumor region more accurately. Higher accuracy has been obtained due to fractional-order derivative. Frequency domain derivative definition has been used in the proposed method due to simplicity and low computational cost. A hardware model of the proposed work has been also presented in the current manuscript. The results obtained have been compared with existing boundary-based and region-based tumor detection and segmentation methods. It has been found that the proposed method is having higher accuracy in benign brain tumor detection and segmentation with low computational cost.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. R.S. Fager, K.V. Peddanarappagari, G.N. Kumar, Pixel-based reconstruction (PBR) promising simultaneous techniques for CT reconstructions. IEEE Trans. Med. Imaging MI 12(1), 4–9 (1993). https://doi.org/10.1109/42.222660

    Article  Google Scholar 

  2. American Brain Tumor Association. http://www.abta.org/

  3. M. Bajpai, P. Munshi, P. Gupta, C. Schorr, M. Maisl, High resolution 3D image reconstruction using algebraic method for X-ray cone-beam geometry over circular and helical trajectories. NDT & E Int. 60, 62–69 (2013). https://doi.org/10.1016/j.ndteint.2013.07.009

    Article  Google Scholar 

  4. B. Mughal, M. Sharif, N. Muhammad, Bi-model processing for early detection of breast tumor in CAD system. Eur. Phys. J. Plus 132(6), 266 (2017). https://doi.org/10.1140/epjp/i2017-11523-8

    Article  Google Scholar 

  5. B. Mughal, N. Muhammad, M. Sharif, Deviation analysis for texture segmentation of breast lesions in mammographic images. Eur. Phys. J. Plus 133(11), 455 (2018). https://doi.org/10.1140/epjp/i2018-12294-4

    Article  Google Scholar 

  6. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3rd edn. (Pearson, London, 2014)

    Google Scholar 

  7. D. Terzopoulos, K. Fleischer, Deformable models. Vis. Comput. 4, 306–331 (1988)

    Article  Google Scholar 

  8. A.M. Hasan, F. Meziane, R. Aspin, H.A. Jalab, Segmentation of brain tumors in MRI images using three-dimensional active contour without edge. Symmetry (2016). https://doi.org/10.3390/sym8110132

    Article  MathSciNet  Google Scholar 

  9. M. Kassa, A. Witkin, D. Terzopoulos, Snakes: active contour models. Int. J. Comput. Vis. 4, 321–331 (1987)

    Google Scholar 

  10. A. Islam, S.M.S. Reza, K.M. Iftekharuddin, Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans. Biomed. Eng. 60(11), 3204–3215 (2013). https://doi.org/10.1109/TBME.2013.2271383

    Article  Google Scholar 

  11. X. Ren, J. Malik, Learning a classification model for segmentation. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 10–17 (2003). https://doi.org/10.1109/ICCV.2003.1238308

  12. W. Pieczynski, Statistical image segmentation. Mach. Graph. Vis. 1, 261–268 (1992)

    Google Scholar 

  13. S.W. Zucker, R.A. Hummel, A three-dimensional edge operator. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 3(3), 321–324 (1981)

    MATH  Google Scholar 

  14. D.G. Morgenthaler, A. Rosenfeld, Multidimensional edge detection by hypersurface fitting. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 3(4), 482–486 (1981). https://doi.org/10.1109/TPAMI.1981.4767134

    Article  Google Scholar 

  15. W. Deng, W. Xiao, H. Deng, J. Liu, MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. In: 2010 3rd International Conference on Biomedical Engineering and Informatics, vol. 1, pp. 393–396 (2010). https://doi.org/10.1109/BMEI.2010.5639536

  16. S.Z. Oo, A.S. Khaing, Brain tumor detection and segmentation using watershed segmentation and morphological operation Int. J. Res. Eng. Technol. 3(3), 367–374 (2014)

    Article  Google Scholar 

  17. E. Abdel-Maksoud, M. Elmogy, R. Al-Awadi, Brain tumor segmentation based on a hybrid clustering technique. Egypt. Inform. J. 16(1), 71–81 (2015). https://doi.org/10.1016/j.eij.2015.01.003

    Article  Google Scholar 

  18. B. Al-Naami, A. Bashir, H. Amasha, J. Al-Nabulsi, A.M. Almalty, Statistical approach for brain cancer classification using a region growing threshold. J. Med. Syst. 35(4), 463–471 (2011). https://doi.org/10.1007/s10916-009-9382-6

    Article  Google Scholar 

  19. K.B. Oldham, J. Spanier, “The Fractional Calulus” Theory and Applications of Differentiation and Integration of Arbitrary Order, 1st edn. (Academic Press, New York, 2006)

    MATH  Google Scholar 

  20. S.K. Chandra, M. Kumar Bajpai, Effective algorithm for benign brain tumor detection using fractional calculus. In: TENCON 2018–2018 IEEE Region 10 Conference, 2018, pp. 2408–2413. https://doi.org/10.1109/TENCON.2018.8650163

  21. T. Wei, Y. Li, Identifying a diffusion coefficient in a time-fractional diffusion equation. Math. Comput. Simul. 151, 77–95 (2018). https://doi.org/10.1016/j.matcom.2018.03.006

    Article  MathSciNet  Google Scholar 

  22. J. Bai, X.C. Feng, Fractional-order anisotropic diffusion for image denoising. IEEE Trans. Image Process. IP 16(10), 2492–2502 (2007). https://doi.org/10.1109/TIP.2007.904971

    Article  ADS  MathSciNet  Google Scholar 

  23. J.J. Koenderink, The structure of images. Biol. Cybern. 50(5), 363–370 (1984)

    Article  MathSciNet  Google Scholar 

  24. S.K. Chandra, M. Kumar Bajpai, Fractional anisotropic diffusion for image denoising. In: 2018 IEEE 8th International Advance Computing Conference (IACC), pp. 344–348 (2018). https://doi.org/10.1109/IADCC.2018.8692094

  25. C.B. Gao, J.L. Zhou, J.R. Hu, F.N. Lang, Edge detection of colour image based on quaternion fractional differential. IET Image Process. 5(3), 261–272 (2011). https://doi.org/10.1049/iet-ipr.2009.0409

    Article  MathSciNet  Google Scholar 

  26. M. Polak, H. Zhang, M. Pi, An evaluation metric for image segmentation of multiple objects. Image Vis. Comput. 27(8), 1223–1227 (2009). https://doi.org/10.1016/j.imavis.2008.09.008

    Article  Google Scholar 

  27. C. Zhao, W. Shi, Y. Deng, A new Hausdorff distance for image matching. Pattern Recogn. Lett. 26(5), 581–586 (2005). https://doi.org/10.1016/j.patrec.2004.09.022

    Article  Google Scholar 

  28. S.K. Chandra, M.K. Bajpai, Mesh free alternate directional implicit method based three dimensional super-diffusive model for benign brain tumor segmentation. Comput. Math. Appl. (2019). https://doi.org/10.1016/j.camwa.2019.02.009

    Article  MathSciNet  Google Scholar 

  29. B.H. Menze et al., The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging (2014). https://doi.org/10.1109/TMI.2014.2377694

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Pritee Khanna, Convener, Computer Vision and Image Processing Lab, Indian Institute of Information Technology, Design & Manufacturing Jabalpur, for supplying computational support to carry out experiments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saroj Kumar Chandra.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chandra, S.K., Bajpai, M.K. Efficient three-dimensional super-diffusive model for benign brain tumor segmentation. Eur. Phys. J. Plus 135, 419 (2020). https://doi.org/10.1140/epjp/s13360-020-00414-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1140/epjp/s13360-020-00414-8

Navigation