A modified fuzzy c-means algorithm for MR brain image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4633 LNCS, Issue , 2007, Pages 866-877

  Szilágyi, László ^a,b   Szilágyi, Sándor M ^b   Benyó, Zoltán ^a  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

^b SAPIENTIA HUNGARIAN UNIVERSITY OF TRANSYLVANIA   (Romania)

Author keywords

Context dependent filter; Feature extraction; Fuzzy c means algorithm; Image segmentation; Magnetic resonance imaging; Noise elimination

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; MAGNETIC RESONANCE IMAGING;

CONTEXT DEPENDENT FILTER; FUZZY C MEANS ALGORITHM; NOISE ELIMINATION;

IMAGE SEGMENTATION;

EID: 37849038370     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74260-9_77     Document Type: Conference Paper

References (15)

1. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21, 193-199 (2002)
2. Bezdek, J.C., Pal, S.K.: Fuzzy models for pattern recognition. IEEE Press, Piscataway, NJ (1991)
3. Cai, W., Chen, S., Zhang, D.Q.: Fast and robust fuzzy c-means algorithms incorporating local information for image segmentation. Patt. Recogn. 40, 825-838 (2007)
4. Chen, S., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE. Trans. Syst. Man. Cybern. Part. B 34, 1907-1916 (2004)
5. Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Comp. Med. Imag. Graph. 30, 9-15 (2006)
6. p norm distances. IEEE Trans. Fuzzy Syst. 8, 576-582 (2000)
7. Internet Brain Segmentation Repository, http://www.cma.mgh.harvard.edu/ ibsr
8. Pham, D.L., Prince, J.L.: Partial volume estimation and the fuzzy c-means algorithm. In: Proc. Int. Conf. Imag. Proc., pp. 819-822 (1998)
9. Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag. 18, 737-752 (1999)
10. Pham, D.L.: Unsupervised tissue classification in medical images using edge-adaptive clustering. In: Proc. Ann. Int. Conf. IEEE EMBS, Cancún, vol. 25, pp. 634-637 (2003)
11. Ruan, S., Jaggi, C., Xue, J.H., Fadili, M.J., Bloyet, D.: Brain tissue classification of magnetic resonance images using partial volume modeling. IEEE Trans. Med. Imag. 19, 1179-1187 (2000)
12. Siyal, M. Y., Yu, L. : An intelligent modified fuzzy c-means based algorithm for bias field estimation and segmentation of brain MRI. Patt. Recogn. Lett. 26, 2052-2062 (2005)
13. Szilágyi, L., Benyó, Z., Szilágyi, S.M., Adam, H.S.: MR brain image segmentation using an enhanced fuzzy C-means algorithm. In: Proc. Ann. Int. Conf. IEEE EMBS, Cancún, vol. 25, pp. 724-726 (2003)
14. Szilágyi, L.: Medical image processing methods for the development of a virtual endoscope. Period. Polytech. Ser. Electr. Eng. 50(1-2), 69-78 (2006)
15. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 20, 45-57 (2001)