Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

MACRo 2015

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

1 Issue per year

Open Access
See all formats and pricing
In This Section

An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects

László Lefkovits
  • Department of Electrical Engineering, Faculty of Technical and Human Sciences, Sapientia University, Tg. Mureş
  • Email:
/ Szidónia Lefkovits
  • Department Informatics, Faculty of Science and Letters, “Petru Maior” University, Tg. Mureş
  • Email:
/ Mircea-Florin Vaida
  • Department of Communications, Technical University of Cluj-Napoca
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. [Crossref]

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

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

  • [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. [Crossref] [Web of Science]

  • [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.

  • [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, 1997

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

  • [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.

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

  • [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 Science]

  • [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.

  • [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. [Crossref]

  • [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. [Crossref]

  • [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. 1998

  • [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 Science]

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

  • [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.

  • [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.

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, ISSN (Online) 2247-0948, DOI: https://doi.org/10.1515/macro-2015-0008. Export Citation

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

Comments (0)

Please log in or register to comment.
Log in