International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 7, No. 11, 8 Oct. 2015
Cover page and Table of Contents: PDF (size: 1167KB)
Enhancing the Quality of Medical Images Containing Blur Combined with Noise Pair
Full Text (PDF, 1167KB), PP.16-25
Views: 0 Downloads: 0
Author(s)
Index Terms
Deblurring, denoising, curvelet transform, bayesian thresholding, augmented lagrangian method
Abstract
In many fields, images become a useful tool containing data of which medical image is an example. The diagnosis depends on the skills of the doctors and image clarity. In the real world, most of medical images consist of noise and blur. This problem reduces the quality of images and causes difficulties for doctors. Most of the tasks of increasing the quality of medical images are deblurring or denoising process. This is the difficult problem in medical image processing, because it must keep the edge features and avoid the loss of information. In case of a medical image which contains noise combined with blur, it is more difficult. In this paper, we have proposed a method for increasing the quality of medical images in case that blur combined with noise pair is available in medical images. The proposed method is divided into two steps: denoising and deblurring. We use curvelet transform combined with bayesian thresholding for the denoising step and use the augmented lagrangian method for the deblurring step. For demonstrating the superiority of the proposed method, we have compared the results with the other recent methods available in literature.
Cite This Paper
Nguyen Thanh Binh, Vo Thi Hong Tuyet,"Enhancing the Quality of Medical Images Containing Blur Combined with Noise Pair", IJIGSP, vol.7, no.11, pp.16-25, 2015. DOI: 10.5815/ijigsp.2015.11.03
Reference
[1]G. Strang, Wavelets and dilation equations: A brief introduction, SIAM Review, Vol.31, No.4, 1989.
[2]Tim Edwards, Discrete Wavelet Transforms: Theory and Implementation, 1992.
[3]Marcin Kociolek, Andrzej Materka, Michal Strzelecki, Piotr Szczypínski, Discrete Wavelet transform – derived features for digital image texture analysis, Proc. Of International Conference on Signals and Electronic Systems, pp. 163—168, 2001.
[4]N.T.Binh, Ashish Khare, Image Denoising, Deblurring and Object Tracking, A new Generation wavelet based approach, LAP LAMBERT Academic Publishing, 2013.
[5]Minh N. Do and Martin Vetterli, The contourlet transform: an efficient directional multiresolution image representation, IEEE Trans. Img. Processing, pp. 2091—2106, 2005.
[6]Arthur L. da Cunha, Jianping Zhou and Minh N. Do, Nonsubsampled Contourlet Transform: Theory, Design, and Applications, IEEE Trans. Img. Proc, pp.3089—3101, 2005.
[7]Arthur L. da Cunha, J. Zhou and Minh N. Do, Nonsubsampled Contourlet Transform: Filter design and applications in denoising, 2006.
[8]E. J. Candes, Ridgelets: Theory and Applications, Stanford University, 1998.
[9]B. Zhang, J. M. Fadili, and J. L. Starck, Wavelets, ridgelets and curvelets for poisson noise removal, IEEE Transactions on Image Processing, pp.1093—1108, 2008.
[10]D. L. Donoho and M. R. Duncan, Digital curvelet transform: Strategy, implementation and experiments, Rpoc. SPIE, Vol. 4056, pp. 12—29, 2000.
[11]Starck J L, Candès E J, Donoho D L, The curvelet transform for image denoising, IEEE Trans. Image Processing, pp. 670—684, 2002.
[12]Binh NT, Khare A. Multilevel threshold based image denoising in curvelet domain, Journal of computer science and technology, pp. 632—640, 2010.
[13]Stanley H. chan, Ramsin Khoshabeh, Kristofor B. Gibson, Philip E. Gill and Truong Q. Nguyen, An Augmented Lagrangian Method for Total Variation Video Restoration, IEEE Trans. Image Process, Vol. 20, No. 11, pp 3097—3111, 2011.
[14]F. Abramovich, T. Sapatinas, B. W. Silverman, Wavelet thresholding via a Bayesian approach, J. R. Statist. Soc. B, pp. 725—749, 1998.
[15]Sitara K and Remya S, Image deblurring in bayesian framework using template based blur estimation, The International Journal of Multimedia & Its Applications (IJMA), Vol. 4, No. 1, 2012.
[16]Mingwei Chui, Youquian Feng, Wei Wang, Zhengchao Li, Xiaodong Xu, Image Denoising Method with Adaptive Bayes threshold in Nonsubsampled Contourlet Domain, American Applied Science Research Institute, 2012.
[17]J. M Lina and M. Mayrand, Complex Daubechies Wavelets, Journal of Applied and Computational Harmonic Analysis, Vol. 2, pp. 219—229, 1995.
[18]Ashish Khare and Uma Shanker Tiwary, A new method for deblurring and denoising of medical images using complex wavelet transform, IEEE, 2005.
[19]E. J. Candes, L. Demanet, D.L. Donoho, L. Ying, Fast Discrete Curvelet Transforms, Multiscale Modeling and Simulation, Vol.5, pp. 861—899, 2006.
[20]A. Khare, U.S Tiwary, Symmetric Daubechies Complex Wavelet Transform and its application to denoising and deblurring, WSEAS Transactions on signal processing, Vol. 2, pp 738—745, 2006.
[21]Wei Zhang, Fei Yu, Hong-mi Guo, Improved adaptive wavelet threshold for image denoising, Control and Decision Conference, Chinese, pp. 5958-5963, 2009.
[22]Jennifer Ranjani J., Chithra M. S. Bayesian denoising of ultrasound images using heavy-tailed Levy distribution, IET Image Processing, Volume 9, issue 4, p. 338 – 345, 2015.
[23]L. Zhang, X. Li, D. Zhang, Image denoising and zooming under the linear minimum mean square-error estimation framework, IET Image Processing, Volume 6, issue 3, p. 273 – 283, 2012.
[24]Xie Cong-Hua, Chang Jin-Yi, Xu Wen-Bin, Medical image denoising by generalised Gaussian mixture modelling with edge information, IET Image Processing, Volume 8, issue 8, p. 464 – 476, 2014.
[25]N.T.Binh, V.T.H.Tuyet, P.C.Vinh, Increasing the quality of medical images based on the combination of filters in ridgelet domain, Nature of Computation and Communication, Vol 144, ISSN 1867-8211, Springer, pp 320—331, 2015.
[26]V.T.H.Tuyet, N.T.Binh, Reducing impurities in medical images based on curvelet domain, Nature of Computation and Communication, Vol 144, ISSN 1867-8211, Springer, pp 306—319, 2015.