An Efficient Approach for Denoising of CT-Images Using EMD and Dual Tree Complex Wavelet Packets

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Computed tomography (CT) images are generally corrupted by several noises from the measurement process complicating the automatic feature extraction and analysis of clinical data. To achieve the best possible diagnosis it is important that medical images be sharp, clear, and free of noise and artifacts. While the technologies for acquiring digital medical images continue to improve, resulting in images of higher and higher resolution and quality, noise remains an issue for many medical images. Removing noise in these digital images remains one of the major challenges in the study of medical imaging. A variety of literatures have been developed to solve the problem of medical images denoising which is a significant stage in an automatic diagnosis system. In this paper, we propose a new image denoising technique using EMD and Dual Tree Complex Wavelet Packets. Here, histon process is used in order to overcome the smoothing filter type and it will not affect the lower dimensions. We have used two noises, like as Gaussian and salt & pepper for the proposed technique. The performance of the proposed image denoising technique is evaluated on the five CT images using the PSNR and SDME. For comparison analysis, our proposed denoising technique is compared with the existing work in various noise levels. From the results, we can conclude that the proposed denoising technique has shown the SDME of 48.33 but the existing technique show the PSNR of 39.84 for salt & pepper noise.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Denoising; CT; EMD; Dual Tree Complex Wavelet Packet (DTCWP); PSNR; SDME

Full Text:



O. Tischenko, C. Hoeschen, and E. Buhr, “An artifact-free structure saving noise reduction using the correlation between two images for threshold determination in the wavelet domain,” in Medical Imaging 2005: Image Processing. Proceedings of the SPIE., J. M. Fitzpatrick and J. M. Reinhardt, Eds., vol. 5747, pp. 1066-1075, April 2005.

Anja Borsdorf, Rainer Raupach, Thomas Flohr and Joachim Hornegger, “Wavelet based Noise Reduction in CT-Images using Correlation Analysis”, IEEE transactions on medical imaging, Vol. 27, No.12, 2008.

N. Huang, Z. Shen, S. Long, M. Wu, H. Shih, Q. Zheng, N. Yen, C. Tung, H. Liu, The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London, Vol: 454, pp: 903–995, 1998.

G.Y. Chen, B.Kégl, ”Image denoising with complex ridgelets,” Pattern Recognition,Vol. 40,pp.578-585,2007.

João M. Sanches, Jacinto C. Nascimento and Jorge S. Marques “Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation”, IEEE Transactions On Image Processing, Vol. 17, No. 9, September 2008.

Faten Ben Arfia, Mohamed Ben Messaoud, Mohamed Abid, “A New Image denoising Technique Combining the Empirical Mode Decomposition with a Wavelet Transform Technique,” 17th International Conference on Systems, Signals and Image Processing,2010.

Guangming Zhang,Zhiming Cui, Jianming Chen and Jian Wu,"CT Image De-noising Model Based on Independent Component Analysis and Curvelet Transform, “ Journal Of Software, Vol. 5, No. 9, September 2010.

Syed Amjad Ali,Srinivasan Vathsal,K. Lal kishore, "An Efficient Denoising Technique for CT Images using Window-based Multi-Wavelet Transformation and Thresholding, " European Journal of Scientific Research,Vol.48, No.2, pp.315-325,2010.

G. Landi, E.Loli Piccolomini,"An efficient method for nonnegatively constrained Total Variation-based denoising of medical images corrupted by Poisson noise," Computerized Medical Imaging and Graphics ,Vol.36,pp. 38- 46,2012.

Sachin D, Ruikar and Dharmpal D Doye, "Wavelet Based Image Denoising Technique", International Journal of Advanced Computer Science and Applications, Vol. 2, No. 3, pp. 49-53, 2011.

Shanshan Wang, Yong Xia, Qiegen Liu, Jianhua Luo, Yuemin Zhu, David Dagan Feng, "Gabor feature based nonlocal means filter for textured image denoising," J. Vis. Commun. Image R., Vol.23, pp.1008-1018, 2012.

Ehsan Nadernejad, Mohsen Nikpour, "Image denoising using new pixon representation based on fuzzy filtering and partial differential equations, " Digital Signal Processing,Vol.22,pp.913-922,2012.

V Naga Prudhvi Raj, Dr T Venkateswarlu,"Denoising of medical images using dual tree complex wavelet transform, Procedia echnology,Vol.4,pp.238-244,2012.

Jianhua Luo, Yuemin Zhu,"Denoising of medical images using a reconstruction-average mechanism, "Digital Signal Processing, Vol.22, pp.337-347, 2012.

R.Sivakumar, ” Denoising of computer tomography images using curvelet transform”, ARPN-JEAS, Vol.2, No.1, pp. 21 - 26, February 2007.

Sudipta Roy, Nidul Sinha, Asoke K. Sen, “A New Hybrid Image Denoising Method”, International Journal of Information Technology and Knowledge Management, Vol. 2, No. 2, pp. 491 - 497, December 2010.

Shutao Li, Leyuan Fang and Haitao Yin, “An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising”, IEEE Transactions On Biomedical Engineering, Vol. 59, No. 2, February 2012.

V N Prudhvi Raj and Dr. T Venkateswarlu, “Denoising Of Medical Images Using Image Fusion Techniques”, Signal & Image Processing:An International Journal, Vol. 3, No. 4, August 2012.

Syed Amjad Ali, Srinivasan Vathsal, Lal Kishore, “CT Image denoising technique using GA aided window-based multiwavelet transformation & thresholding with the incorporation of an effective quality enhancement method”, International Journal of Digital Content Technology and its Applications (IJDCTA), Vol.4, No.4, pp. 75 - 87, July 2010.

Abdolhossein Fathi and Ahmad Reza Naghsh-Nilchi, “Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function”, IEEE Transactions On Image Processing, Vol. 21, No. 9, September 2012.

Sudipta Roy, Nidul Sinha, Asoke K Sen, “An Efficient Denoising Model based on Wavelet and Bilateral Filters”, International Journal of Computer Applications ,Vol. 53,No. 10,pp. 28- 35, September 2012.

Karen Panetta, Yicong Zhou, Sos Agaian, and Hongwei Jia, "Nonlinear Unsharp Masking for Mammogram Enhancement," IEEE Transactions On Information Technology In Biomedicine, Vol. 15, No. 6, November 2011.

Zhao, J., Yang, J., Qiang, Y., Wang, Q., Lung CT image segmentation based on combined multi-scales watershed method and region growing method, (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 587-592.

Selva Bhuvaneswari, K., Geetha, P., Tumor, edema and atrophy segmentation of brain MRI with wavelet transform and semantic features, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1243-1254.

T. Nawaz, S. Baig, A.Khan, The Performance Comparison of Coded WP-OFDM and DFT-OFDM in Frequency Selective Rayleigh Fading Channel, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (6), pp. 500-505.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize