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Authors
Szilagyi, Laszlo; Szilagyi, Sandor Miklos; Benyo, Balazs; Benyo, Zoltan

Identifier
10.3182/20090812-3-DK-2006.00035

Index Terms
Biomedical imaging systems; Functional imaging and data modelling; Biomedical signal processing

Abstract
Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a hybrid c-means clustering approach to replace the FCM algorithm found in several existing solutions. The novel clustering model is assisted by a pre-filtering technique for Gaussian and impulse noise elimination, and a smoothening filter that helps the c-means algorithm at the estimation of inhomogeneity as a slowly varying additive or multiplicative noise. The slow variance of the estimated INU is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show that the proposed method provides more accurate and more efficient segmentation than the FCM based approach. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

References

Ahmed, M.N., Yamany, S.M., Mohamed, N. and Farag, A.A. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data.  IEEE Trans. Med. Imag., 21, 193–199
Axel, L., Costanini, J., and Listerud, J. (1987) Inhomogeneity correction in surface-coil MR imaging. Amer. J. Roentgenol., 148, 418–420
Barni, M., Capellini, V., and Mecocci, A. (1996) Comments on a possibilistic approach to clustering. IEEE Trans. Fuzzy Systems 4, 393–396
Bezdek, J.C. (1981) Pattern recognition with fuzzy objective function algorithms. Plenum: New York, NY
Brinkmann, B.H., Manduca, A. and Robb, R.A. (1998) Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Trans. Med. Imag., 17, 161–171
Cai, W., Chen, S., and Zhang, D.Q. (2007) Fast and robust fuzzy c-means algorithms incorporating local information for image segmentation. Patt. Recogn. 40, 825–838
Fan, J. L., Zhen, W. Z., and Xie, W. X. (2003) Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Letters 24, 1607–1612. 
Krishnapuram, R., and Keller, J. M. (1993) A possibilistic approach to clustering. IEEE Trans. Fuzzy Systems, 1, 98–110
Leemput, K.V., Maes, F., Vandermeulen, D. and Suetens, P. (1999) Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imag., 18, 885–896
Liew, A.W.C. and Hong, Y. (2003) An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imag., 22, 1063–1075
Pal, N. R., Pal, K., Keller, J.M., and Bezdek, J. C. (2005) A possibilistic fuzzy c-means clustering algorithms. IEEE Trans. Fuzzy Systems, 13, 517–530
Pham, D.L. and Prince, J.L. (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag., 18, 737–752
Siyal, M.Y. and Yu, L. (2005) An intelligent modified fuzzy C-means based algorithm for bias field estimation and segmentation of brain MRI. Patt. Recogn. Lett., 26, 2052–2062
Steinhaus H (1956) Sur la division des corp materiels en parties. Bulletin de l’Academie Polonaise des Science C1 III., IV, 801–804
Szilágyi, L. (2006) Medical Image Processing Methods for the Development of a Virtual Endoscope. Periodica Polytechnica Ser. El. Eng., 50, 69–78
Szilágyi, L., Szilágyi, S.M. and Benyó, Z. (2007) Efficient Feature Extraction for Fast Segmentation of MR Brain Images. Lect. Notes Comp. Sci., 4522, 611–620
Szilágyi, L. (2008a) Novel medical image processing methods based on fuzzy logic. PhD thesis, BUTE Budapest
Szilágyi, L., Dávid, L., Szilágyi, S.M., Benyó, B., and Benyó, Z. (2008b) Improved Intensity Inhomogeneity Correction Techniques in MR Brain Image Segmentation. Proc. 17th IFAC World Congress, Seoul, 9625–9630
Szilágyi, L., Szilágyi, S.M. and Benyó, Z. (2008c) Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm. Lect. Notes Comp. Sci., 5285, 146–157 
Vovk, U., Pernuš, F., and Likar, B. (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imag., 26, 405–421
Wells, W. M., Grimson, W. E. L., Kikinis, R., and Jolesz, F. A. (1996) Adaptive segmentation of MRI data. IEEE Trans. Med. Imag., 15, 429–442
Worth, A. (2000) Internet Brain Segmentation Repository. http://www.cma.mgh.harvard.edu/ibsr.
Zhang, Y., Brady, M., and Smith, S. (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag., 20, 45–57