Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation


  • B Prasanthi
  • Dr N. Nagamalleswararao






Image segmentation, Rough sets, fuzzy sets and rough sets, magnetic resonance, Intuitionistic Fuzzy sets, noise reduction and image intensity.


Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.




[1] Anupama Namburu, Srinivas kumar Samay, Srinivasa Reddy Edara, “Rough fuzzy rough set-based MR brain image segmentationâ€,Proceedings in Applied Rough Computing xxx (2016) xxx–xxx. © 2016 Elsevier B.V. All rights reserved.

[2] S. Ramathilagam, R. Pandiyarajan, A. Sathya, R. Devi, S.R. Kannan,†Modified fuzzy c-means algorithm for segmentation of T1–T2-weighted brain MRIâ€, © 2010 Elsevier B.V. All rights reserved , Journal of Computational and Applied Mathematics 235 (2011) 1578–1586.

[3] K.V. Leemput, F. Maes, D. Vandermeulen, P. Suetens, Automated model based bias field correction of MR images of the brain, IEEE Trans. Med. Imaging 18 (1999) 885–896.

[4] M.N. Ahmed, N.A. Mohamed, A.A. Farag, T. Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imaging 21 (2002) 193–199.

[5] R.L. Cannon, J.V. Dave, J.C. Bezdek, Efficient implementation of the fuzzy c-means clustering algorithms, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (2) (1986) 248–255.

[6] Zujun Hou, A review on MR image intensity in homogeneity correction, Int. J. Biomed. Imaging (2006) 1–11. Article ID 49515.

[7] B.R. Condon, J. Patterson, D. Wyper, Image nonuniformity in magnetic resonance imaging: its magnitude and methods for its correction, Br. J. Radiol. 60 (1987) 83–87.

[8] M. Tincher, C.R. Meyer, R. Gupta, D.M. Williams, Polynomial modelling and reduction of spatial body-coil spatial in homogeneity, IEEE Trans. Med. Imaging 12 (1993) 361–365.

[9] S. Lai, M. Fang, A new variational shape-from orientation approach to correcting intensity inhomogeneities in MR images, in: Proc. of Workshop on Biomedical Image Analysis CVPR98, 1998, pp. 56–63.

[10] S.E. Moyher, D.B. Vigneron, S.J. Nelson, Surface coil MR imaging of the human brain with an analytic reception profile correction, J. Magn. Reson. Imaging 5 (1995) 139–144.

[11] S. Krinidis, V. Chatzis, A robust fuzzy local information c-means clusteringalgorithm, IEEE Trans. Image Process. 19 (5) (2010) 1328–1337.

[12] C. Li, J.C. Gore, C. Davatzikos, Multiplicative intrinsic component optimization(MICO) for MRI bias field estimation and tissue segmentation, Magn. Reson.Imaging 32 (7) (2014) 913–923.

[13] Z. Pawlak, Rough set approach to knowledge-based decision support, Eur. J.Oper. Res. 99 (1) (1997) 48–57.

[14] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst. 17(2–3) (1990) 191–209.[15] P. Lingras, C. West, Interval set clustering of web users with rough k-means, J.Intell. Inf. Syst. 23 (1) (2004) 5–16.

[15] P. Maji, S.K. Pal, Rough set based generalized fuzzy c-means algorithm andquantitative indices, IEEE Trans. Syst. Man Cybern. B 37 (6) (2007)1529–1540.

[16] P. Maji, S.K. Pal, RFCM: a hybrid clustering algorithm using rough and fuzzysets, Fundam. Inform. 80 (4) (2007) 475–496

[17] P. Maji, S.K. Pal, Maximum class separability for rough-fuzzy c-means basedbrain MR image segmentation, in: Transactions on Rough Sets IX, Springer,2008, pp. 114–134.

[18] S. Mitra, W. Pedrycz, B. Barman, Shadowed c-means: integrating fuzzy andrough clustering, Pattern Recognit. 43 (4) (2010) 1282–1291.

[19] Z. Ji, Q. Sun, Y. Xia, Q. Chen, D. Xia, D. Feng, Generalized rough fuzzy c-meansalgorithm for brain MR image segmentation, Comput. Methods Progr.Biomed. 108 (2) (2012) 644–655.

[20] P. Lingras, G. Peters, Applying rough set concepts to clustering, in: Rough Sets:Selected Methods and Applications in Management and Engineering,Springer, 2012, pp. 23–37.

[21] M. Shabir, M. Irfan Ali, T. Shaheen, Another approach to Rough rough sets,Knowl. Based Syst. 40 (2013) 72–80.

[22] H. Verma, R. Agrawal, A. Sharan, An improved intuitionistic fuzzy c-meansclustering algorithm incorporating local information for brain imagesegmentation, Appl. Rough Comput. 46 (2015) 543–557.

[23] H. Verma, R. Agrawal, Possibilistic intuitionistic fuzzy c-means clusteringalgorithm for MRI brain image segmentation, Int. J. Artif. Intell. Tools 24 (05)(2015), 1550016-1-1550016-24.

[24] Y.K. Dubey, M.M. Mushrif, K. Mitra, Segmentation of brain MR images usingrough set based intuitionistic fuzzy clustering, Biocybern. Biomed. Eng. 36(2016) 413–426.

[25] G. Peters, F. Crespo, P. Lingras, R. Weber, Rough clustering-fuzzy and roughapproaches and their extensions and derivatives, Int. J. Approx. Reason. 54 (2)(2013) 307–322.

[26] P. Maji, R. Biswas, A.R. Roy, Rough set theory, Comput. Math. Appl. 45 (4) (2003)555–562.

[27] D. Molodtsov, Rough set theory first results, Comput. Math. Appl. 37 (4) (1999)19–31.

[28] H. Aktas¸ , N. C¸ a˘gman, Rough sets and Rough groups, Inf. Sci. 177 (13) (2007)2726–2735.

[29] P.K. Maji, R. Biswas, A. Roy, Fuzzy Rough sets, J. Fuzzy Math. 9 (3) (2001)589–602.

[30] F. Feng, X. Liu, V. Leoreanu-Fotea, Y.B. Jun, Rough sets and Rough rough sets, Inf.Sci. 181 (6) (2011) 1125–1137.

[31] N. Meng, X.H. Zhang, K.Y. Qin, Rough rough fuzzy sets and Rough fuzzy rough sets,Comput. Math. Appl. 62 (12) (2011) 4635–4645.

[32] K Hari Kishore, K Durga Koteswara Rao, G Manvith, K Biswanth, P Alekhya “Area, Power and Delay Efficient 2-bit Magnitude Comparator using Modified GDI Technique in Tanner 180nm Technology†International Journal of Engineering and Technology(UAE), ISSN No: 2227-524X, Vol No: 7, Issue No: 2.8, Page No: 222-226, March 2018.

[33] P Ramakrishna, K. Hari Kishore, “Design of Low Power 10GS/s 6-Bit DAC using CMOS Technology “International Journal of Engineering and Technology(UAE), ISSN No: 2227-524X, Vol No: 7, Issue No: 1.5, Page No: 226-229, January 2018.

[34] P. Sahithi K Hari Kishore, E Raghuveera, P. Gopi Krishna “DESIGN OF VOLTAGE LEVEL SHIFTER FOR POWER-EFFICIENT APPLICATIONS USING 45nm TECHNOLOGY†International Journal of Engineering and Technology(UAE), ISSN No: 2227-524X, Vol No: 7, Issue No: 2.8, Page No: 103-108, March 2018.

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How to Cite

Prasanthi, B., & N. Nagamalleswararao, D. (2018). Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation. International Journal of Engineering & Technology, 7(3.12), 73–80. https://doi.org/10.14419/ijet.v7i3.12.15866
Received 2018-07-19
Accepted 2018-07-19
Published 2018-07-20