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, and they generally have difficulties when INU reaches high amplitudes. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of INU, by modeling it as a slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothing 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 accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.
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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)
Axel, L., Costanini, J., Listerud, J.: Inhomogeneity correction in surface-coil MR imaging. Amer. J. Roentgenol. 148, 418–420 (1987)
Bezdek, J.C., Pal, S.K.: Fuzzy models for pattern recognition. IEEE Press, Piscataway (1991)
Brinkmann, B.H., Manduca, A., Robb, R.A.: Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Trans. Med. Imag. 17, 161–171 (1998)
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)
Internet Brain Segmentation Repository, http://www.cma.mgh.harvard.edu/ibsr
Johnston, B., Atkins, M.S., Mackiewich, B., Anderson, M.: Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans. Med. Imag. 15, 154–169 (1996)
Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imag. 18, 885–896 (1999)
Li, X., Li, L.H., Lu, H.B., Liang, Z.G.: Partial volume segmentation of brain magnetic resonace images based on maximum a posteriori probability. Med. Phys. 32, 2337–2345 (2005)
Liew, A.W.C., Hong, Y.: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imag. 22, 1063–1075 (2003)
Nagy, L., Benyó, B.: Filtering and contrast enhancement on subtracted direct digital angiograms. In: Ann. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 26, pp. 1533–1536. San Francisco (2004)
Pham, D.L., Prince, J.L.: An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneity. Patt. Recogn. Lett. 20, 57–68 (1999)
Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag. 18, 737–752 (1999)
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.: Medical image processing methods for the development of a virtual endoscope. Period. Polytech. Ser. Electr. Eng. 50(1–2), 69–78 (2006)
Szilágyi, L., Szilágyi, S.M., Benyó, Z.: Efficient Feature Extraction for Fast Segmentation of MR Brain Images. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 611–620. Springer, Heidelberg (2007)
Vovk, U., Pernuš, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imag. 26, 405–421 (2007)
Wells, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A.: Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 15, 429–442 (1996)
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)
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Szilágyi, L., Szilágyi, S.M., Dávid, L., Benyó, Z. (2008). Multi-stage FCM-Based Intensity Inhomogeneity Correction for MR Brain Image Segmentation. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_55
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DOI: https://doi.org/10.1007/978-3-540-87559-8_55
Publisher Name: Springer, Berlin, Heidelberg
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