Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Correction of Inhomogeneous MR Images Using Multiscale Retinex
Wen-Hung Chao, Chien-Wen Cho, Yen-Yu Shih, You-Yin Chen, Chen Chang
Pages - 1 - 16     |    Revised - 15-06-2007     |    Published - 30-06-2007
Volume - 1   Issue - 1    |    Publication Date - June 2007  Table of Contents
Magnetic resonance imaging, Surface coils, Single-scale Retinex, multiscale retinex, Peak signal-to-noise ratio, Contrast-to-noise ratio
A new method for enhancing the contrast of magnetic resonance images (MRI) by retinex algorithm is proposed. It can correct the blurrings in deep anatomical structures and inhomogeneity of MRI. Multiscale retinex (MSR) employed SSR with different weightings to correct inhomogeneities and enhance the contrast of MR images. The method was assessed by applying it to phantom and animal images acquired on MRI scanner systems. Its performance was also compared with other methods based on two indices: (1) the peak signal-to-noise ratio (PSNR) and (2) the contrast-to-noise ratio (CNR). Two indices, including PSNR and CNR, were used to evaluate the performance of correction of inhomogeneity in MR images. The PSNR/CNR of a phantom and animal images were 11.8648 dB/2.0922 and 11.7580 dB/2.1157, respectively, which were higher or very close to the results of wavelet algorithm. The retinex algorithm successfully corrected a nonuniform grayscale, enhanced contrast, corrected inhomogeneity, and clarified the deep brain structures of MR images captured by surface coils and outperformed histogram equalization, local histogram equalization, and a waveletbased algorithm, and hence may be a valuable method in MR image processing.
CITED BY (4)  
1 Gambaruto, A. M. (2015). Processing the image gradient field using a topographic primal sketch approach. International journal for numerical methods in biomedical engineering, 31(3).
2 Al-Ameen, Z., & Sulong, G. (2015). A new algorithm for improving the low contrast of computed tomography images using tuned brightness controlled single-scale Retinex. Scanning, 37(2), 116-125.
3 Alizadeh, M., Talebpour, A., Soltanian-Zadeh, H., & Aghamiri, S. M. R. (2012, May). Effects of improved Adaptive Gamma Correction Method on Wireless Capsule Endoscopy images: Illumination compensation and edge detection. In Electrical Engineering (ICEE), 2012 20th Iranian Conference on (pp. 1544-1548). IEEE.
4 Shang, F., Zhai, J., Song, S., Song, Z., & Wang, C. (2010). Performance improvement of phase change memory cell by using a cerium dioxide buffer layer. Applied Physics Letters, 96(20), 3504.
1 Google Scholar
2 ScientificCommons
3 Academic Index
4 CiteSeerX
5 refSeek
7 Socol@r
8 ResearchGATE
9 Bielefeld Academic Search Engine (BASE)
10 Scribd
11 WorldCat
12 SlideShare
14 PdfSR
15 NCTU Institutional Repository
1 Y. Ding and P. Horster, “Undetectable On-Line Password Guessing Attacks”. ACM Operating System Review, vol. 29, no. 4, pp. 77–86, October1995
2 B. R. Conway, M. S. Livingstone. “Spatial and temporal properties of cone signals in alert macaque primary visual cortex”. J Neuroscience, 26(42):10826–10846 2006
3 Z. Cho, J. P. Jones and M. Singh. “Foundations of medical imaging”, New York: JohnWiley and Sons, (1993)
4 B. R. Conway. “Spatial structure of cone inputs to color cells in alert macaque primary visual cortex”. J Neuroscience, 21:2768–2783 2001
5 H. S. Zadeh, J. P. Windham, D. J. Peck, A. E. Yagle “A comparative analysis of several transforms for enhancement and segmentation of magnetic resonance image scene sequences” IEEE Trans Med Imag, 11:302–318 1992
6 E. B. Boskamp. “Improved surface coil imaging in MR: decoupling of the excitation and receiver coils”. Radiology,157(2):449–452,1985
7 M. L. Wood, M. J. Shivji and P. L. Stanchev. “Planar motion correction with use of k-space data acquired in Fourier MR imaging”. J Magn Reson Imaging, 5(1):57–64,1995
8 R. A. Zoroofi, Y. Sato, S. Tamura, H. Naito and L. Tang. “An improved L method for MRI artifact correction due to translational motion in the imaging plane”. IEEE Trans Med Imag, 14:471–479,1995
9 J. G. Sled, A. P. Zijdenbos and AC Evans. “A nonparametric method for automatic correction of intensity nonuniformity in MRI data”. IEEE Trans Med Imag, 17(1):87–97,1998
10 C. B. Ahn, Y. C. Song and D. J. Park. “Adaptive template filtering for signal-to-noise ratio enhancement in magnetic resonance imaging”. IEEE Trans Med Imag, 18(6):549–556,1999.
11 M. Styner, C. Brechbuhler, G. Szekely, and G. Gerig. “Parametric estimate of intensity inhomogeneities applied to MRI”. IEEE Trans Med Imag,; 19(3):153–165, 2000
12 B. Likar, M. A. Viergever and F. Pernus, “Restrospective correction of MR intensity inhomogeneity by information minimization”. IEEE Trans Med Imag, 20:1398–1410, 2001.
13 F. H. Lin, Y. J. Chen, J. W. Belliveau and L. L. Wald. “A wavelet-based approximation of surface coil sensitivity profile for correction of image intensity inhomogeneity and parallel imaging reconstruction”. Human Brain Mapp, 19(2):96–111, 2003
14 C. Han, T. S. Hatsukami and C. Yuan. “A multi-scale method for automatic correction of intensity non-uniformity in MR images”. J Magn Reson Imaging, 13(3):428–436, 2001
15 Z. Hou. “A review on MR image intensity inhomogeneity correction”. International Journal of Biomedical Imaging, pp. 1–11, 2006
16 R. R. Edelman, et al. “Surface coil MR imaging of abdominal viscera. Part 1: theory, technique, and initial results”. Radiology, 157(2):425–430, 1985
17 E. A. Vokurka, N. A. Watson, Y. Watson, NA Thacker and A. Jackson “Improved high resolution MR imaging for surface coils using automated intensity non-uniformity correction: feasibility study in the Orbit”. J Magn Reson Imaging, 14(5):540–546, 2001
18 R. Ouwerkerk, R. G. Weiss and P. A. Bottomley. “Measuring human cardiac tissue sodium concentrations using surface coils, adiabatic excitation, and twisted projection imaging with minimal T2 losses”. J Magn Reson Imaging, 21(5):546–555, 2005
19 C. M. Collins, W. Liu, J. Wang, R. Gruetter, J. T. Vaughan, K. Ugurbil, M. B. Smith. “Temperature and SAR calculations for a human head within volume and surface coils at 64 and 300 M Hz”. J Magn Reson Imaging, 19:650–656, 2004
20 J. Wosik, L. M. Xie, K. Nesteruk, L. Xue, J. A. Bankson and J. D. Hazle. “Superconducting single and phased-array probes for clinical and research MRI”. IEEE Trans Appl Supercon, 13(2):1050–1055, 2003
21 S. D. Chen, A. R. Ramli. “Preserving brightness in histogram equalization based contrast enhancement techniques”. Digit Sig Proc, 14(5):413–428, 2004
22 enhancement techniques”. Digit Sig Proc, 14(5):413–428, 2004 22. V Caselles, JL Lisani, JM Morel and G. Sapiro. “Shape preserving local histogram modification”. IEEE Trans Imag Proc, 8(2):220–230, 1999
23 H. D. Cheng, X. J. Shi. “A simple and effective histogram equalization approach to image enhancement”. Digit Sig Proc, 14(2):158–170, 2004
24 Y. Sun, D. Parker. “Small vessel enhancement in MRA images using local maximum mean processing”. IEEE Trans Imag Proc, 10(11):1687–1699, 2001
25 J. Y. Kim, L. S. Kim, S. H. Hwang. “An advanced contrast enhancement using partially overlapped sub-block histogram equalization”. IEEE Trans Cir & Sys for Video Tech, 11(4):475– 484, 2001
26 J. Tang, E. Peli and S Acton. “Image enhancement using a contrast measure in compressed domain”. IEEE Sig Proc Letters, 10(10):289–292, 2003
27 M Eramian, D Mould. “Histogram equalization using neighborhood metrics”. Proceedings of the 2nd Canadian Conference on Computer and Robot Vision, pp. 397–404, 2005
28 E. Land. “An alternative technique for the computation of the designator in the retinex theory of color vision”. Proc Natl Acad Sci U S A., 83(10):3078–3080, 1986
29 A. Moore, J. Allman, R. M. Goodman. “A real-time neural system for color constancy”. IEEE Trans Neural Networks, 2(2):237–247, 1991
30 A. Moore, G. Fox, J. Allman and R. M. Goodman. “A VLSI neural network for color constancy,” in Advances in Neural Information Processing 3”. D. S. Touretzky, R. Lippman and E. S. Mateo, CA: Morgan Kaufmann, pp. 370–376, 1991
31 A. C. Hurlbert, T. Poggio, “Synthesizing a color algorithm from examples”. Science, 239(4839):482–485, 1988
32 A. C. Hurlbert. “The computation of color”. PhD dissertation, Mass. Inst. Technol., Cambridge, MA, 1989
33 D. J. Jobson, Z. Rahman and G. A. Woodell. “Properties and performance of a center/surround retinex”. IEEE Trans Imag Proc, 6(3):451–462, 1997
34 D. J. Jobson, Z. Rahman and G. A. Woodell. “A multiscale retinex for bridging the gap between color images and the human observation of scenes”. IEEE Trans Imag Proc, 6(7):965– 976, 1997
35 D. E. Bowker, R. E. Davis, D. L. Myrick, K. Stacy and W. L. Jones, “Spectral reflectances of natural targets for use in remote sensing studies”. NASA Ref Pub, 1985.
36 D. H. Brainard, B. A. Wandell. “An analysis of the retinex theory of color vision”. J Opt Soc Amer A, 3(10):1651–1661,1986
37 G. W. Wei. “Generalized perona malik-equation for image restoration”. IEEE Sig Proc Letters, 6(7):165–167, 1999
Mr. Wen-Hung Chao
- Taiwan
Dr. Chien-Wen Cho
- Taiwan
Mr. Yen-Yu Shih
- Taiwan
Mr. You-Yin Chen
- Taiwan
Dr. Chen Chang
- Taiwan