In automated image processing the intensity inhomogeneity of MR images causes significant errors. In this work we analyze three algorithms with the purpose of intensity inhomogeneity correction. The well-known N3 algorithm is compared to two more recent approaches: a modified level set method, which is able to deal with intensity inhomogeneity and it is, as well, compared to an adaptation of the fuzzy c-means clustering with intensity inhomogeneity compensation techniques. We evaluate the outcomes of these three algorithms with quantitative performance measures. The measurements are done on the bias fields and on the segmented images. We consider normal brain images obtained from the Montreal Simulated Brain Database.
 Arnold, J. B., Liow, J. S., Schaper, K. A., Stern, J. J., Sled, J. G., Shattuck, D. W., and Rottenberg, D. A. “Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects”. NeuroImage, vol. 13, no. 5, pp. 931-943, 2001.
 Ashburner, J., and Friston, K. J. “Unified segmentation”. Neuroimage, vol. 26 no. 3, pp. 839-851, 2005.
 [Mni00] BrainWeb: Simulated Brain Database http://brainweb.bic.mni.mcgill.ca/brainweb/ [Accessed 2015]
 Boyes, R. G., Gunter, J. L., Frost, C., Janke, A. L., Yeatman, T., Hill, D. L., and Fox, N. C. “Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils”. Neuroimage, vol. 39, no. 4, pp. 1752-1762, 2008.
 Chua, Z. Y., Zheng, W., Chee, M. W., and Zagorodnov, V. “Evaluation of performance metrics for bias field correction in MR brain images”. Journal of Magnetic Resonance Imaging, vol. 29, no. 6, pp. 1271-1279, 2009.
 Guillemaud, R., and Brady, M. “Estimating the bias field of MR images”. Medical Imaging, IEEE Transactions on, vol.16, no. 3, pp.238-251, 1997
 Insight Segmentation and Registration Toolkit (ITK) http://www.itk.org/ [Accessed 2015]
 Lewis, E. B., and Fox, N. C. “Correction of differential intensity inhomogeneity in longitudinal MR images”. Neuroimage, vol. 23, no. 1, pp. 75-83, 2004.
 Li, C. webpage http://www.engr.uconn.edu/~cmli/ [Accessed 2015]
 Li, C., Huang, R., Ding, Z., Gatenby, J., Metaxas, D. N., and Gore, J. C. “A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI”. Image Processing, IEEE Transactions on, vol. 20 no. 7, pp. 2007-2016, 2011.
 Mumford D. and Shah J., “Optimal approximations by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math., vol. 42, no. 5, pp. 577-685, 1989.
 Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A., and Leahy, R. M. “Magnetic resonance image tissue classification using a partial volume model”. NeuroImage, vol. 13, no. 5, pp.856-876, 2001.
 Siyal M.Y. and Yu L., “An intelligent modified fuzzy c-means based algorithm for bias field estimation and segmentation of brain MRI”, Pattern Recognition Letters, vol. 26, no.13, 2052-2062, 2005.
 Sled J. G., Zijdenbos A. P., and Evans A. C., “A nonparametric method for automatic correction of intensity nonuniformity inMRI data,” IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998
 Szilágyi, L., Szilágyi, S. M., and Benyó, B. “Efficient inhomogeneity compensation using fuzzy c-means clustering models”. Computer methods and programs in biomedicine, vol.108, no. 1, pp. 80-89, 2012.
 Szilágyi, L. “Novel image processing methods based on fuzzy logic”, Scientia Publishing House , Cluj-Napoca, 2009.
 Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., and Gee, J. C. “N4ITK: improved N3 bias correction”. Medical Imaging, IEEE Transactions on, vol. 29, no. 6, pp.1310-1320, 2010.
 Vovk, U., Pernus, F., and Likar, B. “A review of methods for correction of intensity inhomogeneity in MRI”. Medical Imaging, IEEE Transactions on, vol. 26, no.3, 405-421, 2007.