access icon free Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation

Intuitionistic fuzzy sets (IFSs), rough sets are efficient tools to handle uncertainty and vagueness present in images and recently are combined to segment medical images in the presence of noise and intensity non homogeneity (INU). These hybrid algorithms are sensitive to initial centroids, parameter tuning and dependency with the fuzzy membership function to define the IFS. In this paper, a novel clustering algorithm, namely generalized rough intutionistic fuzzy c-means (GRIFCM) is proposed for brain magnetic resonance (MR) image segmentation avoiding the dependency with the fuzzy membership function. In this algorithm, each pixel is categorized into three rough regions based on the thresholds obtained by the image data by minimizing the noise. These regions are used to create IFS. The distance measure based on IFS eliminate's the influence of noise and INU present in the image producing accurate brain tissue segmentation. The proposed algorithm is evaluated through simulation and compared it with existing k-means (KM), fuzzy c-means (FCM), Rough fuzzy c-means (RFCM), Generalized rough fuzzy c-means (GRFCM), soft rough fuzzy c-means (SRFCM) and rough intuitionistic fuzzy c-means (RIFCM) algorithms. Experimental results prove the superiority of the proposed algorithm over the considered algorithms in all analyzed scenarios.

Inspec keywords: pattern clustering; biological tissues; medical image processing; image segmentation; brain; image denoising; biomedical MRI; fuzzy set theory; neurophysiology; rough set theory

Other keywords: intensity non-homogeneity; magnetic resonance brain image segmentation; medical image segmentation; generalised rough intuitionistic fuzzy c-means; noise minimisation; noise influence elimination; initial centroids; cluster standardisation; fuzzy membership function dependency; FCM; Intuitionistic fuzzy set; clustering algorithm; IFS; hybrid algorithm; rough set; brain tissue segmentation; uncertainty handling; parameter tuning

Subjects: Combinatorial mathematics; Combinatorial mathematics; Computer vision and image processing techniques; Medical magnetic resonance imaging and spectroscopy; Biomedical magnetic resonance imaging and spectroscopy; Optical, image and video signal processing; Biophysics of neurophysiological processes; Biology and medical computing; Patient diagnostic methods and instrumentation; Algebra, set theory, and graph theory

References

    1. 1)
      • 21. Mushrif, M.M., Ray, A.K.: ‘A IFS histon based multithresholding algorithm for color image segmentation’, IEEE Signal Proc. Lett., 2009, 16, (3), pp. 168171.
    2. 2)
      • 5. Pal, N.R., Pal, K., Keller, J.M., et al: ‘A possibilistic fuzzy c-means clustering algorithm’, IEEE Trans. Fuzzy Syst., 2005, 13, (4), pp. 517530.
    3. 3)
      • 18. Atanassov, K.T.: ‘Intuitionistic fuzzy sets’, Fuzzy Sets Syst., 1986, 20, (1), pp. 8796.
    4. 4)
      • 17. Anupama, N., Kumar, S.S., Reddy, E.S.: ‘Soft fuzzy rough set-based MR brain image segmentation’, Appl. Soft Comput., 2016, 54, pp. 456466.
    5. 5)
      • 14. Maji, P., Pal, S.K.: ‘Rough set based generalized fuzzy c-means algorithm and quantitative indices’, IEEE Trans. Syst. Man Cybern., 2007, 37, (6), pp. 15291540.
    6. 6)
      • 1. Tou, J.T., Gonzalez, R.C.: ‘Pattern recognition’ (Reading, Addison-Wesley, MA, 1974).
    7. 7)
      • 12. Lingras, P., West, C.: ‘Interval set clustering of web users with rough k-means’, J. Intell. Inf. Syst., 2004, 23, (1), pp. 516.
    8. 8)
      • 2. Modha, D.S., Spangler, W.S.: ‘Feature weighting in k-means clustering’, Mach. Learn., 2003, 52, (3), pp. 217237.
    9. 9)
      • 36. IBSR: ‘The Internet brain segmentation repository’. Available at http://www.cma.mgh.harvard.edu/ibsr/, accessed 7 November 2013.
    10. 10)
      • 19. De, S.K., Biswas, R., Roy, A.R.: ‘An application of intuitionistic fuzzy sets in medical diagnosis’, Fuzzy Sets Syst., 2001, 117, (2), pp. 209213.
    11. 11)
      • 3. Bezdek, J.C., Ehrlich, R., Full, W.: ‘FCM: the fuzzy c-means clustering algorithm’, Comput. Geosci., 1984, 10, (2), pp. 191203.
    12. 12)
      • 13. Maji, P., Pal, S.K.: ‘RFCM: a hybrid clustering algorithm using rough and fuzzy sets’, Fundam. Inform., 2007, 80, (4), pp. 475496.
    13. 13)
      • 32. Pham, D.L.: ‘Spatial models for fuzzy clustering’, Comput. Vis. Image Underst., 2001, 84, (2), pp. 285297.
    14. 14)
      • 7. Cai, W., Chen, S., Zhang, D.: ‘Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation’, Pattern Recognit., 2007, 40, (3), pp. 825838.
    15. 15)
      • 10. Pawlak, Z.: ‘Rough set approach to knowledge-based decision support’, Eur. J. Oper. Res., 1997, 99, (1), pp. 4857.
    16. 16)
      • 4. Hall, L.O., Bensaid, A.M., Clarke, L.P., et al: ‘A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain’, IEEE Trans. Neural Netw., 1992, 3, (5), pp. 672682.
    17. 17)
      • 30. Dubey, Y.K., Mushrif, M.M., Mitra, K.: ‘Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering’, Biocybern. Biomed. Eng., 2016, 36, (2), pp. 413426.
    18. 18)
      • 34. Szmidt, E.: ‘Distances and similarities in intuitionistic fuzzy sets’ (Springer, 1st edn, 2014).
    19. 19)
      • 16. Mitra, S., Pedrycz, W., Barman, B.: ‘Shadowed c-means: integrating fuzzy and rough clustering’, Pattern Recognit., 2010, 43, (4), pp. 12821291.
    20. 20)
      • 6. Chuang, K.-S., Tzeng, H.-L., Chen, S., et al: ‘Fuzzy c-means clustering with spatial information for image segmentation’, Comput. Med. Imaging Graph., 2006, 30, (1), pp. 915.
    21. 21)
      • 27. Balasubramaniam, P., Ananthi, V.: ‘Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm’, Nonlinear Dyn., 2016, 83, (1-2), pp. 849866.
    22. 22)
      • 37. ‘MPRAGE – magnetizaiton-prepared rapid gradient-echo imaging’. Available at http://www.mrtip.com/serv1.php?type=isimg, accessed 1 August 2016.
    23. 23)
      • 38. McAuliffe, M.: ‘Medical image processing, analysis, and visualization (MIPAV)’, Natl. Inst. Health, 2009, 4, (0).
    24. 24)
      • 25. Ananthi, V., Balasubramaniam, P., Lim, C.P.: ‘Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions’, Pattern Recognit., 2014, 47, (12), pp. 38703880.
    25. 25)
      • 15. Maji, P., Pal, S.K.: ‘Maximum class separability for rough-fuzzy c-means based brain MR image segmentation’, in (EDs.): ‘Transactions on Rough Sets IX’ (Springer, 2008, 1st edn.), pp. 114134.
    26. 26)
      • 35. Web, B.: ‘Simulated brain database’, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill, 2004. Available at: http://brainweb.bic.mni.mcgill.ca/brainweb, accessed 13 March 2015.
    27. 27)
      • 29. Verma, H., Agrawal, R.: ‘Possibilistic intuitionistic fuzzy c-means clustering algorithm for MRI brain image segmentation’, Int. J. Artif. Intell. Tools, 2015, 24, (05), pp. 1550016-11550016-24.
    28. 28)
      • 8. Krinidis, S., Chatzis, V.: ‘A robust fuzzy local information C-means clustering algorithm’, IEEE Trans. Image Process., 2010, 19, (5), pp. 13281337.
    29. 29)
      • 9. Li, C., Gore, J.C., Davatzikos, C.: ‘Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation’, Magn. Reson. Imaging, 2014, 32, (7), pp. 913923.
    30. 30)
      • 20. Xu, Z., Chen, J., Wu, J.: ‘Clustering algorithm for intuitionistic fuzzy sets’, Inf. Sci., 2008, 178, (19), pp. 37753790.
    31. 31)
      • 28. Verma, H., Agrawal, R., Sharan, A.: ‘An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation’, Appl. Soft Comput., 2015, 46, pp. 543557.
    32. 32)
      • 11. Dubois, D., Prade, H.: ‘Rough fuzzy sets and fuzzy rough sets*’, Int. J. Gen. Syst., 1990, 17, (2-3), pp. 191209.
    33. 33)
      • 31. Ji, Z., Sun, Q., Xia, Y., et al: ‘Generalized rough fuzzy c-means algorithm for brain MR image segmentation’, Comput. Methods Programs Biomed., 2012, 108, (2), pp. 644655.
    34. 34)
      • 23. Chaira, T.: ‘A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set’, Appl. Soft Comput., 2012, 12, (4), pp. 12591266.
    35. 35)
      • 33. Pedrycz, W., Rai, P.: ‘Collaborative clustering with the use of fuzzy c-means and its quantification’, Fuzzy Sets Syst., 2008, 159, (18), pp. 23992427.
    36. 36)
      • 22. Chaira, T.: ‘A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images’, Appl. Soft Comput., 2011, 11, (2), pp. 17111717.
    37. 37)
      • 24. Melo-Pinto, P., Couto, P., Bustince, H., et al: ‘Image segmentation using Atanassov's intuitionistic fuzzy sets’, Expert Syst. Appl., 2013, 40, (1), pp. 1526.
    38. 38)
      • 26. Ananthi, V., Balasubramaniam, P., Kalaiselvi, T.: ‘A new fuzzy clustering algorithm for the segmentation of brain tumor’, Soft Comput., 2016, 20, (12), pp. 48594879.
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