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MACRo 2015

Proceedings of the 5th International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics

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An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects

László Lefkovits / Szidónia Lefkovits / Mircea-Florin Vaida
Published Online: 2015-05-09 | DOI: https://doi.org/10.1515/macro-2015-0008


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.

Keywords : MR image intensity inhomogeneity; bias field; tissue segmentation; performance metrics


  • [1] 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.CrossrefGoogle Scholar

  • [2] Ashburner, J., and Friston, K. J. “Unified segmentation”. Neuroimage, vol. 26 no. 3, pp. 839-851, 2005.CrossrefGoogle Scholar

  • [3] [Mni00] BrainWeb: Simulated Brain Database http://brainweb.bic.mni.mcgill.ca/brainweb/ [Accessed 2015]Google Scholar

  • [4] 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.CrossrefWeb of ScienceGoogle Scholar

  • [5] 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.Google Scholar

  • [6] Guillemaud, R., and Brady, M. “Estimating the bias field of MR images”. Medical Imaging, IEEE Transactions on, vol.16, no. 3, pp.238-251, 1997Google Scholar

  • [7] Insight Segmentation and Registration Toolkit (ITK) http://www.itk.org/ [Accessed 2015]Google Scholar

  • [8] 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.Google Scholar

  • [9] Li, C. webpage http://www.engr.uconn.edu/~cmli/ [Accessed 2015]Google Scholar

  • [10] 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.Web of ScienceGoogle Scholar

  • [11] 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.Google Scholar

  • [12] 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.CrossrefGoogle Scholar

  • [13] 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.CrossrefGoogle Scholar

  • [14] 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. 1998Google Scholar

  • [15] 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.Web of ScienceGoogle Scholar

  • [16] Szilágyi, L. “Novel image processing methods based on fuzzy logic”, Scientia Publishing House , Cluj-Napoca, 2009.Google Scholar

  • [17] 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.Google Scholar

  • [18] 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. Google Scholar

About the article

Received: 2015-01-12

Revised: 2015-02-25

Published Online: 2015-05-09

Published in Print: 2015-03-01

Citation Information: MACRo 2015, Volume 1, Issue 1, Pages 79–90, ISSN (Online) 2247-0948, DOI: https://doi.org/10.1515/macro-2015-0008.

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© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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