A Multi-Scale Framework for Bias Field Estimation in MRI Brain Images

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Intensity inhomogeneity is an artifact in MR brain images and causes intensity variation of same tissues on the basis of location of the tissue within the image. It is crucial to minimize this phenomenon to improve the accuracy of the computer-aided diagnosis. Unlike the several methods proposed in the past to minimize intensity inhomogeneity, this proposed method uses a pyramidal decomposition strategy to estimate the bias field in MR brain images. The bias field estimated from the proposed multi-scale framework can be effectively used for intensity inhomogeneity correction of the acquired MR data. The proposed methodology has been tested on simulated database and quantitative analyses in terms of coefficient of variation in grey matter and white matter tissue regions separately and combined coefficient of joint variation are assessed. The qualitative and quantitative analyses on the corrected data indicate that the method is effective for intensity inhomogeneity on brain MR images.

     

     


  • Keywords


    Bias field correction; Gaussian pyramid; Intensity inhomogeneity; Pyramidal decomposition; Wiener filter

  • References


      [1] Vovk, U., Pernus, F. and Likar, B. (2007), A Review of Methods for Correction of Intensity Inhomogeneity in MRI. IEEE Transactions on Medical Imaging 26(3), 405–421.

      [2] Simmons, A., Tofts, P. S., Barker, G. J. and Arridge, S. R. (1994), Sources of intensity nonuniformity in spin echo images at 1.5 T. Magnetic Resonance in Medicine 32(1), 121–128.

      [3] Narayana, P. A., Brey, W. W., Kulkarni, M. V. and Sievenpiper, C. L. (1988),Compensation for surface coil sensitivity variation in magnetic resonance imaging. Magnetic Resonance Imaging 6(3), 271–274.

      [4] Mihara, H., Iriguchi, N. and Ueno, S. (1998), A method of RF inhomogeneity correction in MR imaging. Magnetic Resonance Materials in Physics, Biology and Medicine 7(2), 115–120.

      [5] Brinkmann, B. H., Manduca, A. and Robb, R. A. (1998), Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Transactions on Medical Imaging 17(2), 161–171.

      [6] Lewis, E. B. and Fox, N. C. (2004), Correction of differential intensity inhomogeneity in longitudinal MR images. NeuroImage 23(1), 75–83.

      [7] George, M. M. and Kalaivani, S. (2017), Intensity inhomogeneity correction and tissue segmentation of MR images: A parametric approach. International Journal of Pure and Applied Mathematics 115(9), 409–416.

      [8] Bezdek, J. C., Hall, L. O. and Clarke, L. P. (1993), Review of MR image segmentation techniques using pattern recognition. Medical Physics 20(4),1033–1048.

      [9] Derganc, J., Likar, B. and Pernus, F. (2002), Nonparametric segmentation of multispectral MR images incorporating spatial and intensity information. International Society for Optics and Photonics 391

      [10] Likar, B., Derganc, J. (2002), Segmentation-based retrospective correction of intensity nonuniformity in multispectral MR images. SPIE Medical Imaging, 4684, 1531-1540, http://dx.doi.org/10.1117/12.467120.

      [11] Dawant, B. M., Zijdenbos, A. P. and Margolin, R. A. (1993), Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Transactions on Medical Imaging 12(4),770–781.

      [12] Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Y., Egan, A., Yushkevich, P. A. and Gee, J. C. (2010), N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging 29(6), 1310–1320.

      [13] George, M. M., Kalaivani, S. and Sudhakar, M. S. (2017), A non-iterative multi-scale approach for intensity inhomogeneity correction in MRI. Magnetic Resonance Imaging 42, 43–59.

      [14] Adelson, E. H., Anderson, C H, Bergen, J. R., Burt, P J and Ogden, J M (1984), Pyramid methods in image processing. RCA Engineer 29 (6), 33–41.

      [15] Maragos, P. (1987), Tutorial On Advances In Morphological Image Processing And Analysis. Optical Engineering. International Society for Optics and Photonics, 26(7), 267623.

      [16] Kazubek, M. (2003),Wavelet domain image denoising by thresholding and Wiener filtering. IEEE Signal Processing Letters 10(11), 324–326.

      [17] Keys, R. (1981), Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing 29( 6),1153–1160.

      [18] Cocosco, C. A., Cocosco, C. A., Kollokian, V., Kwan, R. K.-S., Pike, G. B. and Evans, A. C. (1997), BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. Neuroimage 5, 425.


 

View

Download

Article ID: 20835
 
DOI: 10.14419/ijet.v7i4.10.20835




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.