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Enhancement of Proton Density Weighted Magnetic Resonance Images using Singular Value Decomposition in Wavelet Domain

K. Kannan


Image enhancement techniques for low contrast medical images using Singular Value Decomposition (SVD) in Discrete Wavelet Transform domain (SVD-DWT) are proposed in the literature. However, shift sensitivity, poor directionality and a lack of phase information are the primary drawbacks of the discrete wavelet transform. This work introduces a novel method of image enhancement (SVD-SWT) using SVD in Stationary Wavelet Transform (SWT) to improve the contrast of Proton Density weighted Magnetic Resonance Images (PD weighted MRI) while preserving the brightness. The low frequency sub bands of PD weighted MRI and the low frequency sub bands of General Histogram Equalized PD weighted MRI are used in the proposed method as a weighted sum of singular value matrices to enhance the contrast of PD weighted MRI. The proposed method is compared with several histogram equalisation methods and improvement techniques employing Singular Value Decomposition and Discrete Wavelet Transform (SVD-DWT). Discrete Entropy (DE), Average Brightness (AB), Pixel Distance (PD), and Standard Deviation (SD) all showed that the proposed method is the best.


Discrete wavelet transform, singular value decomposition, stationary wavelet transform, proton density weighted magnetic resonance images, histogram equalization

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Van Metter Richard L, Jacob Beutel, Kundel Harold L. Handbook of Medical Imaging. Vol. 1. Physics and Psychophysics. SPIE Digital Library; 2000.

Imaios. e-MRI. NMR signal and MRI contrast: A long TR and short TE sequence is usually called Proton density-weighted; A short TR and short TE sequence is usually called T1-weighted; A long TR and long TE sequence is usually called T2-weighted. [Online]. Available from

[Online. Available from “”]

Gonzalez RC, Woods RE. Digital image processing second edition. Beijing: Publishing House of Electronics Industry; 2002; 455. 5. Kim YT. Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron. 1997 Feb; 43(1): 1–8.

Wang Y, Chen Q, Zhang B. Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron. 1999 Feb; 45(1): 68–75.

Chen SD, Ramli AR. Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron. 2003 Nov; 49(4): 1301–9.

Ibrahim H, Kong NS. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron. 2007 Nov; 53(4): 1752–8.

Sheet D, Garud H, Suveer A, Mahadevappa M, Chatterjee J. Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans Consum Electron. 2010 Nov; 56(4): 2475–80.

Demirel H, Anbarjafari G, Jahromi MN. Image equalization based on singular value decomposition. In 2008 IEEE 23rd International Symposium on Computer and Information Sciences 2008 Oct 27; 1–5.

Demirel H, Ozcinar C, Anbarjafari G. Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci Remote Sens Lett. 2009 Nov 17; 7(2): 333-7.

Bhandari AK, Kumar A, Padhy PK. Enhancement of low contrast satellite images using discrete cosine transform and singular value decomposition. International Journal of Computer and Information Engineering (IJCIT). 2011 Jul 23; 5(7): 707–13.

Atta R, Ghanbari M. Low‐contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition. IET Image Process. 2013 Jul; 7(5): 472–83.

Shannon CE. A Mathematical Theory of Architecture. Bell Syst Tech J. 1948; 27: 623–56.

Chen Z, Abidi BR, Page DL, Abidi MA. Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic method. IEEE Trans Image Process. 2006 Jul 17; 15(8): 2290–302.

Daubechies I. Ten lectures on wavelets. Society for industrial and applied mathematics; 1992 Jan 1.

Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989 Jul; 11(7): 674–93.

Shensa MJ. The discrete wavelet transform: wedding the a Trous and Mallat algorithms. IEEE Trans Signal Process. 1992 Oct 10; 40(10): 2464–82.


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