Regularized fuzzy c-means method for brain tissue clustering

Pattern Recognition Letters

Volumn 28, Issue 13, 2007, Pages 1788-1794

  Hou, Z ^a,b   Qian, W ^a   Huang, S ^b   Hu, Q ^a   Nowinski, W L ^a  

^a AGENCY FOR SCIENCE   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Clustering; Fuzzy c means; Image segmentation; Noise level estimate; Regularization; Spatial modeling

Indexed keywords

BRAIN TISSUE SEGMENTATION; FUZZY C-MEANS; NOISE LEVEL ESTIMATE; REGULARIZATION; SPATIAL MODELING;

BRAIN; FUZZY LOGIC; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING;

TISSUE ENGINEERING;

EID: 34447500281     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2007.05.004     Document Type: Article

References (27)

1. Banerjee S., Mukherjee D.P., and Majumdar D.D. Fuzzy c-means approach to tissue classification in multimodal medial imaging. Inform. Sci. 115 (1999) 261-279
2. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
3. Brandt M.E., Bohan T.P., Kramer L.A., and Fletcher J.M. Estimation of CSF, white and grey matter volumes in hydrocephatic children using fuzzy clustering of MR images. Comput. Med. Imag. Graphics 18 (1994) 25-34
4. Chiou G., and Hwang J. A neural network-based stochastic active contour model (NNS-SNAKE) for contour finding of distinct features. IEEE Trans. Image Process. 4 (1995) 1407-1416
5. Collins D.L., Zijdenbos A.P., Kollokian V., Sled J.G., Kabani N.J., Holmes C.J., and Evans A.C. Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imag. 17 (1998) 463-468
6. Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet. 3 (1973) 32-57
7. Engl H.W., Hanke M., and Neubauer A. Regularization of Inverse Problems (1996), Kluwer, Dordrecht, The Netherlands
8. Hansen P.C. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Rev. 34 (1992) 561-580
9. Joliot M., and Mazoyer B.M. Three-dimensional segmentation and interpolation of magnetic resonance brain images. IEEE Trans. Med. Imag. 12 (1993) 269-277
10. Karl W.C. Regularization in image restoration and reconstruction. In: Bovik A.C. (Ed). Handbook of Image and Video Processing (2000), Academic Press, San Diego, CA
11. Li, R.P., Mukaidono, M., 1995. A maximum entropy approach to fuzzy clustering. In: Proc. 4th IEEE Internat. Conf. on Fuzzy Systems, Yokohama, Japan, pp. 2227-2232.
12. Li S.Z. On discontinuity-adaptive smoothness priors in computer vision. IEEE Trans. PAMI 17 (1995) 576-586
13. Liang Z., MacFall J.R., and Harrington D.P. Parameter estimation and tissue segmentation from multispectral images. IEEE Trans. Med. Imag. 13 (1994) 441-449
14. Miyamoto, S., Mukaidono, M., 1997. Fuzzy c-means as a regularization and maximum entropy approach. In: Proc. 7th Internat. Fuzzy Systems Association World Congress, Prague, Czech Republic, pp. 86-92.
15. Miyamoto, S., Umayahara, K., 1998. Fuzzy clustering by quadratic regularization. In: Proc. FUZZ-IEEE'98, Anchorage, Alaska, pp. 1394-1399.
16. Miyamoto S., and Umayahara K. Methods in hard and fuzzy clustering. In: Liu Z.Q., and Miyamoto S. (Eds). Soft Computing and Human-Centered Machines (2000), Springer, Tokyo Chapter 5
17. Morozov V.A. Methods for Solving Incorrectly Posed Problems (1984), Springer, Berlin
18. Philips W.E., Velthuizen R.P., Phuphanich S., Hall L.O., Clarke L.P., and Silbiger M.L. Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magn. Reson. Imaging 13 (1995) 277-290
19. Pham D.L. Spatial models for fuzzy clustering. Comput. Vision Image Understanding 84 (2001) 285-297
20. Rajapakse J.C., Giedd J.N., and Rapoport J.L. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imag. 16 (1997) 176-186
21. Rudin L.I., Osher S., and Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D 60 (1992) 259-268
22. Schroeter P., Vesin J., Langenberger T., and Meuli R. Robust parameter estimation of intensity distributions for brain magnetic resonance images. IEEE Trans. Med. Imag. 17 (1998) 172-186
23. Shareef N., Wang D.L., and Yagel R. Segmentation of medical images using LEGION. IEEE Trans. Med. Imag. 18 (1999) 74-91
24. Terzopoulos D. Regularization of inverse problems involving discontinuities. IEEE Trans. PAMI 8 (1986) 613-624
25. Tikhonov A.N. On solving incorrectly posed problems and method of regularization. Dokl. Acad. Nauk USSR 151 (1963) 501-504
26. Tikhonov A.N., and Arsenin V.Y. Solutions of Ill-posed Problems (1977), Winston and Sons, Washington, DC
27. Wahba G. Practical approximate solutions to linear operator equations when the data are noisy. SIAM J. Num. Anal. 14 (1977) 651-667