i-manager Publications

Segmentation Algorithm Using FCM: Application to MRI Brain Image

G. Wiselin Jiji*, L. Ganesan**

*Assist Professor ,Information Technology, Dr. Sivanthi Aditanar College Of Engineering, Tiruchendur

** HOD ,Computer science and engineering, A.C. College of Engg. and Technology, Karaikudi.

Periodicity:August - October'2006
DOI : https://doi.org/10.26634/jfet.2.1.956

Abstract

Segmentation is the most common way to identify various constituents of an image. In medical image visualization and analysis, segmentation is an indispensable step in the processing of images. In this paper, we propose an unsupervised fuzzy based segmentation to derive the homogeneity measure for partitioning the regions of the image during the process of segmentation. The proposed approach proceeds in 2 stages. Initially, a Fuzzy C-Mean algorithm is used to provide a partition of the image. In the second Stage, a decision process by applying a global analysis followed by a fine segmentation which is only focused on ambiguous points. The feasibility and effectiveness of the proposed method is evaluated with the various types of MRI brain images to identify the components of brain, in turn, used to locate the brain tumour and its growth rate.

Keywords

FCM, EucledianDistance, MRI Brain image, Segmentation.

How to Cite this Article?

G. Wiselin Jiji and L. Ganesan (2006). Segmentation Algorithm Using FCM: Application to MRI Brain Image. i-manager’s Journal on Future Engineering and Technology, 2(1), 10-13. https://doi.org/10.26634/jfet.2.1.956

References

[1]. Timothy J.Ross ."Fuzzy Logic with Engineering applications" McGrawHill International Editions.

[2], Angiographic Images," IEEE Trans, on Med. Imaging, vol. 11, pp. 416-429,1992.

[3]. J. C. Bezdek and R F. Castelaz, "Prototype Classification and Feature Selection With Fuzzy Sets," IEEE Trans, on Systems, Man and Cybernetics, vol. SMC-7, pp. 87-92,1977.

[4], N. Boujemaa, G. Stamon, A. Gagalowicz, "Modelisation floue pour la segmentation d'images," 9,me CongrSs AFCET de Reconnaissance de Formes et Intelligence Artificielle, pp. 163-173, France, 1994.

[5], J.C.Bezdek.R.Ehrich and W.Full, 1984 FCM :the fuzzy C-Means Clustering Algorithm.

[6], M.S.Kamel and S.Z.Selim,1994, New algorithms for solving the fuzzy clustering problem.

[7], Freeman WJ (1992) Tutorial in neurobiology: From single neurons to brain chaos. International Journal of Bifurcation and Chaos 2:451 -482.

[8]. D. Metaxas and D. Terzopoulos, \Shape and nonrigid motion estimation trough Physicsbased synthesis," IEEE- PAMI15, pp. 580-591,1993.

[9]J.J-M. Morel and S. Solimini, Variational Methods in Image Segmentation, Birkhauser, Boston, 1994.

[10].D. Mumford and J. Shah, \Optimal approximations by piecewise smooth functions and variational problems," Comm. Pure and App. Math. 42,1989,

[11]. R. Nevatia, A color edge detector and its use in scene segmentation," IEEE Trans.Syst. Man, Cybern., 7, pp. 820-826,1977.

[12].J. Shah, Segmentation by nonlinear di_usion," Proc. IEEE-CVPR, 1991.

[13].J. Shah, Recovery of shapes by evolution of zero- crossings," Technical Report, Math. Dept. Northeastern Univ. Boston MA, 1995.

[14]. H.M. Soner, Motion of a set by the curvature of its boundary," J. of Di_. Equations 101, pp. 313-372,1993.

[15]. W. S. Stiles, A modied Helmholtz line element in brightness-colourspace," Proc. Phys. Soc. (London) 58, p. 41,1946.

[16].G. Berks, D.G. Keyserlingk, J. Jantzen, M. Dotoli, H. Axer, Fuzzy Clustering-A Versatile Mean to Explore Medical Database, ESIT2000, Aachen, Germany.

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Segmentation Algorithm Using FCM: Application to MRI Brain Image

Abstract

Keywords

How to Cite this Article?

References

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