Abstract
Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzy c-means (FCM) or similar clustering mechanisms. Several improvements have been made to the standard FCM algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. This paper presents a modified FCM-based method that targets accurate and fast segmentation in case of mixed noises. The proposed method extracts a scalar feature value from the neighborhood of each pixel, using a context dependent filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards by the histogram-based approach of the enhanced FCM algorithm. Results were evaluated based on synthetic phantoms and real MR images. Test experiments revealed that the proposed method provides better results compared to other reported FCM-based techniques. The achieved segmentation and the obtained fuzzy membership values represent excellent support for deformable contour model based cortical surface reconstruction methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Bezdek, J.C., Pal, S.K.: Fuzzy models for pattern recognition. IEEE Press, Piscataway, NJ (1991)
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)
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)
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)
Hathaway, R.J., Bezdek, J.C., Hu, Y.: Generalized fuzzy c-means clustering strategies using L p norm distances. IEEE Trans. Fuzzy Syst. 8, 576–582 (2000)
Internet Brain Segmentation Repository, http://www.cma.mgh.harvard.edu/ibsr
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)
Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag. 18, 737–752 (1999)
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)
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)
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)
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)
Szilágyi, L.: Medical image processing methods for the development of a virtual endoscope. Period. Polytech. Ser. Electr. Eng. 50(1-2), 69–78 (2006)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szilágyi, L., Szilágyi, S.M., Benyó, Z. (2007). A Modified Fuzzy C-Means Algorithm for MR Brain Image Segmentation. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_77
Download citation
DOI: https://doi.org/10.1007/978-3-540-74260-9_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74258-6
Online ISBN: 978-3-540-74260-9
eBook Packages: Computer ScienceComputer Science (R0)