Skip to main content
SpringerLink
Log in

Partially Supervised Kernel Induced Rough Fuzzy Clustering for Brain Tissue Segmentation

  • APPLIED PROBLEMS
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

In modern imaging diagnosis Magnetic Resonance Imaging (MRI) is possibly one of the widely used effective techniques particularly for brain tissue segmentation. Clustering techniques may not be perfect always. Clustering can be significantly improved by supervising partially. A novel partially supervised kernel induced rough fuzzy clustering is proposed for brain tissue segmentation by employing a small quantity of labeled pixels with constraint seeded policy. Labeled pixels act as the constraints which are utilized to initialize the clustering process and guide the method towards a more accurate partitioning. Kernel trick used here enhances the possibility of linear partition of different complex segments of brain which cannot separate linearly in its original feature space. Whereas, the rough and fuzzy set handles the overlappingness, vagueness and indiscernibility of different tissue regions. A variety of benchmark brain MRI datasets are used for the experiments. The ability of the method is compared with state-of-the-art clustering segmentation techniques and evaluated using different validity indices. Experimental results confirm that the technique considerably enhances the segmentation accuracy with a little quantity of supervision. Enhancement in accuracy gained by the method compared to the other techniques are 0.3, 0.37, 1.15, and 1.03% for IBSR datasets 144, 150, 155, and 167, respectively, and 1.02% for the BrainWeb dataset 85. Statistical impact of the method is confirmed from the paired t-test results.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.

Similar content being viewed by others

REFERENCES

  1. M. C. Wittrock, The Brain and Psychology (Academic Press, 1980).

    Google Scholar 

  2. T. W. Vanderah and D. J. Gould, Nolte’s The Human Brain: An Introduction to Its Functional Anatomy, 7th ed. (Elsevier, 2015).

    Google Scholar 

  3. Mayfield Education and Research Foundation. Anatomy of the Brain. http://www.mayfieldclinic.com/pe-anatbrain.htm. Accessed March 24, 2020.

  4. L. M. Biga, S. Dawson, A. Harwell, R. Hopkins, J. Kaufmann, M. Le Master, P. Matern, D. Quick, K. M. Graham, and J. Runyeon, Anatomy and Physiology. https://openstax.org/details/books/anatomy-and-physiology. Accessed March 24, 2020.

  5. P. Suetens, Fundamentals of Medical Imaging (Cambridge Univ. Press, Cambridge, UK, 2002).

    Google Scholar 

  6. M. A. Haidekker, Medical Imaging Technology (Springer, New York, 2013).

    Book  Google Scholar 

  7. S. Bauer, R. Wiest, L. P. Nolte, and M. Reyes, “A survey of MRI based medical image analysis for brain tumor studies,” Phys. Med. Biol. 58 (13), 97–129 (2013).

    Article  Google Scholar 

  8. P. Maji and S. K. Pal, Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging (John Wiley & Sons, Inc., New Jersey and Canada, 2012).

    Book  Google Scholar 

  9. D. L. Bailey, D. W. Townsend, P. E. Valk, and M. N. Maisey, Positron-Emission Tomography: Basic Sciences (Springer-Verlag, Secaucus, 2005).

    Book  Google Scholar 

  10. A. M. Cormack and G. N. Hounsfield, The Nobel Prize in Physiology or Medicine for the Development of Computer-Assisted Tomography. https://www.nobelprize.org/1979. Accessed March 24, 2020.

  11. Single-Photon Emission-Computed Tomography. http://id.nlm.nih.gov/mesh/D015899. Accessed March 24, 2020.

  12. R. J. English, SPECT: A Primer, 3rd ed. (Soc. Nucl. Med., CNMT, Reston, 2005).

  13. N. P. Alazraki, M. J. Shumate, and D. A. Kooby, A Clinicians Guide to Nuclear Oncology (Soc. Nucl. Med. Mol. Imaging, Reston, 2007).

    Google Scholar 

  14. National Research Council (US) and the Institute of Medicine (US) Committee, Mathematics and Physics of Emerging Dynamic Biomedical Imaging (Natl. Acad. Press, Washington D.C., 1996).

    Google Scholar 

  15. M. A. Balafar, A. R. Ramli, M. I. Saripan, and S. Mashohor, “Review of brain MRI image segmentation methods,” Artif. Intell. Rev. 33 (4), 261–274 (2010).

    Article  Google Scholar 

  16. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer Verlag, New York, 2006).

    MATH  Google Scholar 

  17. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Pearson Prentice Hall, 2007).

    Google Scholar 

  18. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. (Academic Press, New York, 2009).

    MATH  Google Scholar 

  19. H. R. Singleton and G. M. Pohost, “Automatic cardiac MR image segmentation using edge detection by tissue classification in pixel neighborhoods,” Magn. Reson. Med. 37 (3), 418–424 (1997).

    Article  Google Scholar 

  20. I. N. Manousakes, P. E. Undrill, and G. G. Cameron, “Split and merge segmentation of magnetic resonance medical images: Performance evaluation and extension to three dimensions,” Comput. Biomed. Res. 31 (6), 393–412 (1998).

    Article  Google Scholar 

  21. B. N. Subudhi, V. Thangaraj, E. Sankaralingam, and A. Ghosh, “Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation,” Magn. Reson. Imaging 34 (9), 1292–1304 (2016).

    Article  Google Scholar 

  22. S. Banerjee, S. Mitra, and B. Umashankar, “Single seed delineation of brain tumor using multi-thresholding,” Inf. Sci. 330 (4), 88–103 (2016).

    Article  Google Scholar 

  23. J. C. Rajapakse, J. N. Giedd, and J. L. Rapoport, “Statistical approach to segmentation of single channel cerebral MR images,” IEEE Trans. Med. Imaging 16 (2), 176–186 (1997).

    Article  Google Scholar 

  24. A. Banerjee and P. Maji, “Rough-probabilistic clustering and hidden Markov random field model for segmentation of HEp-2 cell and brain MR images,” Appl. Soft Comput. 46, 558–576 (2016).

    Article  Google Scholar 

  25. S. Saha and S. Bandyopadhyay, “MRI brain image segmentation by fuzzy symmetry based genetic clustering technique,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (2007), pp. 4417–4424.

  26. P. Maji and S. Roy, “Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation,” PLOS ONE 10 (4), 1–30 (2015).

    Article  Google Scholar 

  27. G. Vishnuvarthanana, M. P. Rajasekaran, P. Subbaraj, and A. Vishnuvarthanan, “An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images,” Appl. Soft Comput. 38, 190–212 (2016).

    Article  Google Scholar 

  28. J. P. Sarkara, I. Saha, and U. Maulik, “Rough possibilistic type-2 fuzzy c-means clustering for MR brain image segmentation,” Appl. Soft Comput. 46, 527–536 (2016).

    Article  Google Scholar 

  29. S. Ravishankar and Y. Bresler, “MR image reconstruction from highly under sampledk-space data by dictionary learning,” IEEE Trans. Med. Imaging 30 (5), 1028–1041 (2011).

    Article  Google Scholar 

  30. E. S. A. E. Dahshan, H. M. Mohsen, K. Revett, and A. B. M. Salem, “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm,” Expert Syst. Appl. 41 (11), 5526–5545 (2014).

    Article  Google Scholar 

  31. J. Liu, M. Li, J. Wang, F. Wu, T. Liu, and Y. Pan, “A survey of MRI based brain tumor segmentation methods,” Tsinghua Sci. Technol. 19 (6), 578–595 (2014).

    Article  MathSciNet  Google Scholar 

  32. N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the art survey on MRI brain tumor segmentation,” Magn. Reson. Imaging 31 (8), 1426–1438 (2013).

    Article  Google Scholar 

  33. N. Grira, M. Crucianu, and N. Boujemaa, Unsupervised and Semi-Supervised Clustering: A Brief Survey. Technical Report B.P. 105 (INRIA Rocquencourt, 2005).

    Google Scholar 

  34. X. Zhu, Semi-Supervised Learning Literature Survey. Technical Report Computer Sciences TR 1530 (Univ. Wisconsin-Madison, 2008).

    Google Scholar 

  35. S. Basu, A. Banerjee, and R. J. Mooney, “Semi-supervised clustering by seeding,” in Proceedings of the 19th International Conference on Machine Learning (2002), pp. 19–26.

  36. S. Basu, A. Banerjee, and R. J. Mooney, “Active semi-supervision for pairwise constrained clustering,” in Proceedings of the SIAM International Conference on Data Mining (2004), pp. 333–344.

  37. L. H. Son and T. M. Tuan, “Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints,” Eng. Appl. Artif. Intell. 59, 186–195 (2017).

    Article  Google Scholar 

  38. S. Saha, A. K. Alok, and A. Ekbal, “Brain image segmentation using semi-supervised clustering,” Expert Syst. Appl. 52, 50–63 (2016).

    Article  Google Scholar 

  39. A. Halder and N. A. Talukdar, “Brain tissue segmentation using improved kernelized rough-fuzzy c-means with spatio-contextual information from MRI,” Magn. Reson. Imaging 62, 129–151 (2019).

    Article  Google Scholar 

  40. B. Scholkopf and A. J. Smol, Learning with Kernels (MIT Press, Cambridge, 2002).

    Google Scholar 

  41. J. S. Taylor and N. Cristianini, Kernel Method for Pattern Analysis (Cambridge Univ. Press, 2004).

    Book  Google Scholar 

  42. L. A. Zadeh, “Fuzzy sets,” Inf. Control 8 (3), 338–353 (1965).

    Article  MATH  Google Scholar 

  43. Z. Pawlak, “Rough sets,” Int. J. Comput. Inf. Sci. 11 (5), 341–356 (1982).

    Article  MATH  Google Scholar 

  44. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data (Kluwer, Dordrecht, 1991).

    Book  MATH  Google Scholar 

  45. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci. 10 (2–3), 191–203 (1984).

    Article  Google Scholar 

  46. L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek, “A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain,” IEEE Trans. Neural Networks 3 (5), 672–682 (1993).

    Article  Google Scholar 

  47. T. Hofmann, B. Schölkopf, and A. J. Smola, “Kernel methods in machine learning,” Ann. Stat. 36 (3), 1171–1220 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  48. D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans, “Design and construction of a realistic digital brain phantom,” IEEE Trans. Med. Imaging 17 (3), 463–468 (1998).

    Article  Google Scholar 

  49. T. Rohlfing, “Image similarity and tissue overlaps as surrogates for image registration accuracy: Widely used but unreliable,” IEEE Trans. Med. Imaging 31 (2), 153–163 (2012).

    Article  Google Scholar 

  50. C. L. Li, D. B. Goldgof, and L. O. Hall, “Knowledge-based classification and tissue labeling of MR images of human brain,” IEEE Trans. Med. Imaging 12 (4), 740–750 (1993).

    Article  Google Scholar 

  51. P. Maji and S. K. Pal, “RFCM: A hybrid clustering algorithm using rough and fuzzy sets,” Fundam. Inf. 80 (4), 475–496 (2007).

    MathSciNet  MATH  Google Scholar 

  52. A. Halder, “Kernel based rough fuzzy c-means clustering optimized using particle swarm optimization,” in Proceedings of the International Symposium on Advanced Computing and Communication (ISACC) (2015), pp. 41–48.

  53. S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34 (4), 193–199 (2004).

    Article  Google Scholar 

  54. A. Halder and N. A. Talukdar, “Robust brain magnetic resonance image segmentation using modified rough-fuzzy c-means with spatial constraints,” Appl. Soft Comput. 85, 105758 (2019).

    Article  Google Scholar 

  55. S. Das and S. Sil, “Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm,” Inf. Sci. 180 (8), 1237–1256 (2010).

    Article  MathSciNet  Google Scholar 

  56. J. A. Rice, Mathematical Statistics and Data Analysis (Cengage Learning, 2006).

    Google Scholar 

Download references

Funding

There was no any funding to carry out the research.

Author information

Authors and Affiliations

  1. Department of Computer Applications, School of Technology, North-Eastern Hill University, 794002, Meghalaya, India

    Nur Alom Talukdar

  2. Department of Computer Applications, School of Technology, North-Eastern Hill University, 794002, Meghalaya, India

    Anindya Halder

Authors
  1. Nur Alom Talukdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anindya Halder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Nur Alom Talukdar or Anindya Halder.

Ethics declarations

Conflict of interest declared none.

Additional information

Nur Alom Talukdar received the B.Tech. in Computer Science and Engineering and M.Tech. in Information Technology from Gauhati University, Guwahati, and Assam University, Silchar, India, in 2013 and 2015, respectively. He is currently working towards PhD as a Research Scholar in the Dept. of Computer Applications, School of Technology, North-Eastern Hill University, Meghalaya. He has published a number of research articles in internationally reputed journals and refereed conferences. His research interests include, machine learning, pattern recognition, soft computing, and medical image analysis.

Dr. Anindya Halder is presently working as Teacher-in-Charge (HOD) and Assistant Professor in Department of Computer Application, School of Technology, North-Eastern Hill University, Meghalaya, India. He received the Master of Computer Application (MCA) from University of Kalyani, India, in 2005 and PhD in Engineering from Jadavpur University, Kolkata, India, in 2013. He worked (towards Ph.D.) as a Research Scholar at the Center of Soft Computing Research, Indian Statistical Institute (ISI), Kolkata, India during August 2007 to July 2012. Dr. Halder was a Visiting Scientist at Remote Sensing Laboratory, University of Trento, Italy during January 2010–June 2010. He has published a number of research articles in internationally reputed journals and refereed conferences. His research interests include, machine learning, pattern recognition, swarm intelligence, soft computing, remote sensing image analysis, bioinformatics, and medical image analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nur Alom Talukdar, Anindya Halder Partially Supervised Kernel Induced Rough Fuzzy Clustering for Brain Tissue Segmentation. Pattern Recognit. Image Anal. 31, 91–102 (2021). https://doi.org/10.1134/S1054661821010156

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661821010156

Keywords:

Search

Navigation