Computed Tomography Images Restoration Using Anisotropic Diffusion Regularization

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The CT scan imaging system is one of the most interesting non-invasive radiological methods allowing the generation of tomographic images of all parts of the human body. However, CT images are corrupted by noise and blur due to the imperfections and the physical limitations of the imaging systems. Increasing the spatial resolution of these images leads to a good interpretation by the clinician.  In this paper, we propose a new approach to improve the quality of the CT images. Our method is based on the anisotropic diffusion regularisation incorporates an adaptative smoothness constraint in the deconvolution process. That is, the smooth is encouraged in a homogeneous region and discourage across boundaries, in order to preserve significant image details. The blur component is estimated by an iterative blind deconvolution approach and incorporated in the restoration process. Experimental results show a good performance and are very promising for future research.
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CT Images; Image Restoration; Regularization; Blind Deconvolution; PDE; Anisotropic Diffusion

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A. Tikhonov, V. Arsenin, Solutions of Ill-Posed Problems, Washington, DC, Winston and Sons, 1977.

A.C. Likas, N.P. Galatsanos, A Variational Approach for Bayesian Blind Image Deconvolution,”, IEEE Transactions on Signal Processing, Vol. 52 (8), 2004.

A. C. Kak and M. Slaney,(1988), Principles of Computerized Tomographic Imaging, IEEE Press,1988

D. Tshumperlé, R. Dériche, (2002) ”Diffusion PDE’s on Vector Valued Images, Local Approach and GeometricView-point”, IEEE Signal Processing Magazine-Special Issue on Mathematical Methods in Imaging, Vol. 19 ,n°.5,pp.15-25, March 2002.

D. Tschumperle, R. Deriche,’’Diffusion PDE's on Vector-Valued images’’, IEEE Signal Processing Magazine, 19 (5) (2002) pp.15-25

Molina, R. Mateos, J Katsaggelos, A.K. , Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation, IEEE Trans. On Image Processing, 2006, pp.3715-3727.

D.S.C Biggs, M. Andrews, ‘’Asymmetric Iterative Blind Deconvolution of Multiframe Images’’, Proceedings SPIE, 33 (3461) 1998.

D.P.K Lun, T.C.L Chan, T.C. Hsung, D.D. Feng, Y.H Chan, ‘’Efficient Blind Image Restoration Discrete Periodic Radon Transform’’, IEEE Transactions on Image Processing, 13 (2), February 2004.

Li, H.C.;Fan, P.Z; Khan, M.K. Context-adaptive anisotropic diffusion for image denoising, Electronic Letters Vol 48, 2012 , pp.827-829.

F. Benzarti, K. Hamrouni, H. Amiri,” Mammographic Image Restoration using Anisotropic Regularization” IEEE International Conference on Machine Intelligence, ACIDCA-ICMI 2005, Tozeur-Tunisia, November 2005.

S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. San Diego, CA: Academic, 1999.

L.B. Lucy, An Iterative Technique for The Rectification of Observed Images, Astronomical Journal, Vol. 79 (6) (1974) pp:28-37.

Zheng Huang;Jingxin Zhang;Cishen Zhang, Ultrasound image reconstruction by two-dimensional blind total variation deconvolution, IEEE International Conference on Control and Automation, 2009. ICCA2009. pp: 1801 – 1806

M.Mignotte, J.Meunier, S-P.Soucy, C.Janicki, ‘’Comparison of Deconvolution Techniques using a Distribution Mixture Parameter Estimation: Application in Single Photon Emission Computed Tomography Imagery’’, Journal of Electronic Imaging, Vol. 11 (1), January 2002.

M.R. Banham, A.K Katsaggelos, Digital Image Restoration, IEEE Signal Processing Magazine, pp.24-41, Vol 14 (2), March 1997.

Mingjun Wang ; Shuxian Deng, Image Restoration Model of PDE Variation, Second International Conference on Information and Computing Science, 2009. ICIC '09. Vol. 2, pp. 184-187.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern. Anal. Machine Intell., vol.12, pp. 629–639, 1990

P.J. Rousseau , A.M. Leroy, ‘’Robust Regression and outlier detection,’’ New york; Wiley 1987.

R.C. Gonzalez,”Digital Image Processing”, Second Edition, Prentice Hall, 2002.

R. Molina, J. Nunez, F.J. Cortijo, J. Mateos,’’ Image Restoration in Astronomy,’’ IEEE Signal Processing Magazine, March 2001, 11-29

GR. Ayers, J.C. Dainty,”Iterative Blind Deconvolution”,Optics Letters, Vol 13n°7,pp.547-549,1988 [22] Y.L. You, M.Kaveh “Blind Image Restoration by Anisotropic Regularization” IEEE Trans. On IP , 8 (3), March 1999, 396-407

Fahmy, M.F.; Abdel Raheem, G.M.; Mohammed, U.S.; Fahmy, O.F.; “A Fast Iterative Blind image restoration algorithm” 28th IEEE Conference on National Radio Science Conference (NRSC); pp1-8, 2011.

F. Benzarti; H. Amiri, Blind Photographic Images Restoration With Discontinuities Preservation, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), Vol. 4, pp. 609-618, 2012.

F. Benzarti, M. Askri, K. Hamrouni, CT images Restoration, International Conference E-Medical Systems, E-Medisys, Hammamet-Tunisia, October 2008.

Shoulie Xie, Rahardia S, Alternating Direction Method for Balanced Image Restoration, IEEE Trans. On Image Processing, Vol. 21, pp.4557-4567,2012.

Chao Dong;Meihua Xie, A blind image restoration algorithm based on nonlocal means and EM algorithm, International Conference on Audio, Language and Image Processing (ICALIP),pp.485-489,2012.

Li Chen; Kim-Hui Yap, Efficient discrete spatial techniques for blur support identification in blind image deconvolution, IEEE Trans. On Signal Processing,Vol. 54 ,(4): 2006 , Page(s): 1557 – 1562

Fan, H., Zhu, H., Zhu, G., Liu, X., Improvement of wood ultrasonic CT images by using time of flight data normalization, (2011) International Review on Computers and Software (IRECOS), 6 (6), pp. 1079-1083.

Liu, W., An image restoration algorithm based on image fusion, (2012) International Review on Computers and Software (IRECOS), 7 (3), pp. 1245-1249.

Wu, Q., Wang, K., Zuo, W., Total variation-based image restoration using I-divergence, (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 668-672.


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