Abstract
Unsupervised Fuzzy C-Means (FCM) clustering technique has been widely used in image segmentation. However, conventional FCM algorithm, being a histogram-based method when used in classification, has an intrinsic limitation: no spatial information is taken into account. This causes the FCM algorithm to work only on well-defined images with low level of noise. In this paper, a novel improvement to fuzzy clustering is described. The prior spatial constraint, which is defined as refusable level in this paper, is introduced into FCM algorithm through Markov random field theory and its equivalent Gibbs random field theory, in which the spatial information is encoded through mutual influences of neighboring sites. The algorithm is applied to the segmentation of synthetic image and brain magnetic resonance (MR) images (simulated and real) and the classification results show the new algorithm to be insensitive to noise.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tolias, Y.A., Panas, S.M.: On applying spatial constraints in fuzzy image clustering using a fuzzy rule-basedsystem. IEEE Signal Process. Lett. 5(10), 245–247 (1998)
Tolias, Y.A., Panas, S.M.: Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Trans. Systems, Man, Cybernet. A 28(3), 359–369 (1998)
Acton, S.T., Mukherjee, D.P.: Scale space classification using area morphology. IEEE Trans. Image Process 9(4), 623–635 (2000)
Liew, A.W.C., Leung, S.H., Lau, W.H.: Fuzzy image clustering incorporating spatial continuity. IEE Proc. Visual Image Signal Process 147(2), 185–192 (2000)
Pham, D.L.: Spatial Models for Fuzzy Clustering. Computer Vision and Image Understand-ing 84, 285–297 (2001)
Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarity, T.: A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segementation of MRI Data. IEEE trans. On Medical Imaging 21(3), 193–199 (2002)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Heidelberg (2001)
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. Medical Imaging 20(1), 45–57 (2001)
German, S., German, D.: Stochastic relaxation, Gibbs distribution, and the Bayesian Restoration of Images. IEEE Trans. Patter Anal. Machine Intell PAMI-6(6), 721–741 (1984)
Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. On Medical Imaging 18(9), 737–752[2] (1999)
Zhu, C.Z., Jiang, T.Z.: MultiContext fuzzy clustering for separation of brain tissues in magnetic resonance images. NeuroImage 18(3), 685–696 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feng, Y., Chen, W. (2004). Brain MR Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field Theory. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28626-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-28626-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22877-6
Online ISBN: 978-3-540-28626-4
eBook Packages: Springer Book Archive