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Contourlet Transform Based Method For Medical Image Denoising
Abbas Hanon Hassin AlAsadi
Pages - 22 - 31     |    Revised - 31-1-2015     |    Published - 28-2-2015
Volume - 9   Issue - 1    |    Publication Date - January / February 2015  Table of Contents
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KEYWORDS
Medical Image, Denoising, Wavelet Transform, Contourlet Transform.
ABSTRACT
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
CITED BY (1)  
1 Santhosh, B., & Viswanath, K. (2016). Review on Secured Medical Image Processing. In Information Systems Design and Intelligent Applications (pp. 531-537). Springer India.
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1 Do, Minh N., and Martin Vetterli. "Contourlets: a directional multiresolution image representation." Image Processing. 2002. Proceedings. 2002 International Conference on . Vol. 1. IEEE, 2002.
2 Zhou, Z-F., and P-L. Shui. "Contourlet-based image denoising algorithm using directional windows." Electronics Letters 43.2 (2007): 92-93.
3 Do, Minh N., and Martin Vetterli. "The contourlet transform: an efficient directional multiresolution image representation." Image Processing, IEEE Transactions on 14.12 (2005): 2091-2106.
4 Po, DD-Y., and Minh N. Do. "Directional multiscale modeling of images using the contourlet transform." Image Processing, IEEE Transactions on 15.6 (2006): 1610-1620.
5 Tsakanikas, Panagiotis, and Elias S. Manolakos. "Improving 2-DE gel image denoising using contourlets." Proteomics 9.15 (2009): 3877-3888.
6 Liu, Zhe. "Minimum distance texture classification of SAR images in contourlet domain." Computer Science and Software Engineering, 2008 International Conference on . Vol. 1. IEEE, 2008.
7 Esakkirajan, S., et al. "Image compression using contourlet transform and multistage vector quantization." GVIP J 6.1 (2006): 19-28.
8 Rao, Ch Srinivasa, S. Srinivas Kumar, and B. N. Chatterji. "Content based image retrieval using contourlet transform." ICGST-GVIP Journal 7.3 (2007): 9-15.
9 Minh, N. Directional Multiresolution Image Representations, (Doctoral dissertation of Engineering), Computer Engineering, University of Canberra, Australia, 2002.
10 Bhateja, Vikrant, et al. "A modified speckle suppression algorithm for breast ultrasound images using directional filters." ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II . Springer International Publishing, 2014.
11 Hiremath, P. S., Prema T. Akkasaligar, and Sharan Badiger. "Removal of Gaussian Noise in Despeckling Medical Ultrasound Images." The International Journal of Computer Science & Applications (TIJCA) 1.5 (2012).
12 Fayed, Hassan, Mohamed Rizk, and Ahmed Aboul Seoud. "Improved Medical Image Retrieval using Contourlet Techniques Based Interest Points Detector." Canadian Journal on Image Processing and Computer Vision 4.2 (2013).
13 Song, Xiao-yang, et al. "Speckle reduction based on contourlet transform using scale adaptive threshold for medical ultrasound image." Journal of Shanghai Jiaotong University (Science) 13 (2008): 553-558.
14 Hiremath, P. S., Prema T. Akkasaligar, and Sharan Badiger. "Speckle reducing contourlet transform for medical ultrasound images." Int J Compt Inf Engg 4.4 (2010): 284-291.
15 Sivakumar, R., et al. "Image denoising using contourlet transform." Computer and Electrical Engineering, 2009. ICCEE'09. Second International Conference on . Vol. 1. IEEE, 2009.
16 Burt, Peter J., and Edward H. Adelson. "The Laplacian pyramid as a compact image code." Communications, IEEE Transactions on 31.4 (1983): 532-540.
17 Buades, Antoni, Bartomeu Coll, and Jean-Michel Morel. "A review of image denoising algorithms, with a new one." Multiscale Modeling & Simulation 4.2 (2005): 490-530.
18 Saxena, Chandrika, and Deepak Kourav. "Noises and Image Denoising Techniques: A Brief Survey." International Journal of Emerging Technology and Advanced Engineering , 4.3 (2014):878-885.
19 Wang, Zhou, et al. "Image quality assessment: from error visibility to structural similarity." Image Processing, IEEE Transactions on 13.4 (2004): 600-612.
Associate Professor Abbas Hanon Hassin AlAsadi
Faculty of S cience /Department of Computer Science Basra University Basra - Iraq
abbashh2002@gmail.com