Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoising

Murat Ceylan, Ayse Elif Canbilen
  • Murat Ceylan
    Selcuk University,

Abstract

Noise reduces the quality of medical images and raise the difficulties of diagnosis. Although the wavelet transform has already been used in medical noise removal applications extensively, there are many other multi-resolution analysis methods proposed in recent years for denoising. The main goal of this study is comparing the image denoising abilities of some of these methods with wavelet transform. In this paper, image denoising is implemented by a three-stage methodology. Effectiveness of the multiresolution analysis methodologies has been investigated for standard test images beside magnetic resonans, mammography and fundus images. Performances of the transforms are compared by using peak signal to noise ratio, mean square error, mean structural similarity index and feature similarity index. The best results are obtained by tetrolet transform for random and rician noise with the benchmark images. Medical image denoising performance of Tetrolet transform is compared to other multiresolution analysis methods for the first time in the literature with this study. It surpassed ridgelet and haar wavelet transforms while the noise ratio was low. On the other hand, it is seen that curvelet transforms are effectively produce the best results for all rates of noise on medical images.

Keywords

Curvelet transform, image denoising, Ridgelet transform, Tetrolet transform, Wavelet transform

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Submitted: 2017-06-14 11:27:09
Published: 2017-12-12 13:20:45
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References

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693,1989.

I. Daubechies, “Ten lectures on wavelets”,PA: SIAM, Philadelphia, 1992.

M. Lang, H. Guo, J.E. Odegard, C.S. Burrus, R.O. Wells,“Noise reduction using an undecimated discrete wavelet transform,”IEEE Signal Processing Letters, vol. 3,pp. 10-12, 1996.

A. Mojsilovic, M. Popovic, D. Sevic, “Classification of the ultrasound liver images with the 2N x 1-D wavelet transform,”Proceedings of the IEEE International Conference on Image Processing, Laussanne, Switzerland, 1996,pp. 367-370.

I. Delakis, O. Hammad, R.I. Kitney,“Wavelet-based denoising algorithm for images acquired with parallel magnetic resonance imaging (MRI),” Physics in Medicine And Biology, vol. 52, pp. 3741-3751, 2007.

V. Kidsumran, W. Chiracharit, “Contrast enhancement mammograms using denoising in wavelet coefficients,”10th International Joint Conference on Computer Science and Software Engineering, Maha Sarakham, Tayland, 2013, pp. 82-86.

E. Candes, D.L. Donoho, “Ridgelets: the key to high-dimensional intermittency,”Philosophical Transactions of the Royal Society A, vol. 357, pp. 2495–2509, 1999.

M.N. Do, M. Vetterli, “The finite ridgelet transform for image representation,”IEEE Transactions on Image Processing,vol. 12,pp. 16-28, 2003.

X. Wang, “Wrap-around effect removal finite ridgelet transform for multiscale image denoising,”Pattern Recognition, vol. 43, pp. 3693-3698, 2010.

F. Makhlouf, N. Khlifa, H. Besbes, C. B. Amar, B.Soulaiman, “A comparative study of multiresolution methods to reduce the noise in scintigraphic images,”International Conference on Computer Medical Applications, Sousse, Tunis, 2013, pp. 1-5.

H. Yaşar, M. Ceylan, A.E. Öztürk, “Comparison of real and complex-valued versions of wavelet transform, curvelet transform and ridgelet transform for medical image denoising,”IJEMME, vol.3,pp. 427-436, 2013.

E.J. Candes, D.L. Donoho, “Curvelets - a surprisingly effective nonadaptive representation for objects with edges”, in: Cohen A, Rabut C, Schumaker LL, eds. Curve and surface fitting: Saint Malo 1999. Nashville: Vanderbilt University Press, 2000.

D.L. Donoho, M.R. Duncan, “Digital curvelet transform: strategy, implementation and experiments,”Proceedings of SPIE: Wavelet Application VII, vol. 4056,pp. 12-30, 2000, DOI:10.1117/12.381679.

E.J. Candes,L. Demanet, D.L. Donoho, L. Ying, “Fast discrete curvelet transforms,”Multiscale Modeling And Simulation, vol. 5, pp. 861-899, 2006.

J.L. Starck, E.J. Candes, D.L. Donoho, “The curvelet transform for image denoising,”IEEE Transactions on Image Processing, vol. 11, pp. 670-684, 2002.

C. Kamath, A. Gyaourova, I.K. Fodor,“Undecimated curvelet transforms for image denoising”, Center for Applied Scientific Computing, Lawrencelivermore National Laboratory, Tech. Rep. UCRL-ID-150931,2002.

R. Sivakumar, “Denoising of computer tomography images using curvelet transform”,ARPN Journal of Engineering and Applied Sciences, vol. 2,pp. 21-26, 2007.

J. Ma, G. Plonka, “The curvelet transform - a review of recent applications,”IEEE Signal Processing Magazine, vol. 27,pp. 118-133, 2010.

E. Malar, A. Kandaswamy, S.S. Kirthana, D. Nivedhitha, “A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms,”International Conference on Machine Vision And Image Processing, Nadu, India, 2012,pp. 65-68.

S. AlZubi, N. İslam, M. Abbod, “Multi-resolution analysis using curvelet and wavelet transforms for medical imaging,”Medical Measurements and Applications Proceedings, Bari, Italy, 2011,pp. 188-191.

V. V. K. Raju, M. P. Kumar, “Denoising of MRI and X-ray images using dual tree complex wavelet and curvelet transforms,”International Conference on Communication and Signal Processing, Melmaruvathur, India, 2014,pp. 1844-1848.

J. Krommweh, “Tetrolet transform: a new adaptive haar wavelet algorithm for sparse image representation,”Journal of Visual Communication And Image Representation, vol. 21,pp. 364-374, 2010.

M.K. Singh, “Denoising of natural images using the wavelet transform,” M.S. thesis, San Jose State University, 2010.

P. Jain, V. Tyagi, “An adaptive edge-preserving image denoising technique using tetrolet transforms,”The Visual Computer, vol. 31,pp. 657-674, 2014.

M. Ceylan, A.E. Öztürk, “Determining the number of tetrominoe orders for denoising applications performed by tetrolet transform,”IEEE 22nd Signal Processing and Communications Applications Conference, Trabzon, Turkey,2014, pp. 216-219(in Turkish).

M.M.R. Mohan, V.S. Sheeba, “A novel method of medical image denoising using bilateral and NLM filtering,”3rd International Conference on Advances in Computing and Communications, Cochin, India, 2013, pp. 186-191.

H. Yu, L. Zhao, “An efficient denoising procedure for magnetic resonance imaging,”The 2nd International Conference on Bioinformatics and Biomedical Engineering, Shangai, China, 2008, pp. 2628-2630.

V. N. P. Raj, T.Venkateswarlu, “Denoising of MR images using adaptive multiresolution subband mixing,”IEEE International Conference on Computational Intelligence and Computing Research, Enathi, India, 2013, pp. 1-6.

S. Li, H. Yin, L. Fang, “Group-sparse representation with dictionary learning for medical image denoising and fusion,”IEEE Transactions on Biomedical Engineering, vol. 59, pp. 3450-3459, 2012.

S. Bhatnagar, R.C. Jain, “Different denoising techniques for medical images in wavelet domain,”International Conference on Signal Processing and Communication, Noida, India, 2013,pp. 325-329.

L. Dai, Y. Zhang,Y. Li, “Image denoising using BM3D combining tetrolet prefiltering,”Information Technology Journal, vol. 12, pp. 1995-2001, 2013.

Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,”IEEE Transactions on Image Processing, vol. 13,pp. 600-612, 2004.

D. Brunet, E.R. Vrscay, Z. Wang, “On the mathematical properties of the structural similarity index,”IEEE Transactions on Image Processing, vol. 21, pp. 1488-1499, 2012.

N. Thakur, S. Devi, “A new method for color image quality assessment,”International Journal of Computer Applications, vol. 15,pp. 10-17, 2011.

L. Zhang, L. Zhang, X. Mou, D. Zhang, “FSIM: a feature similarity index for image quality assessment,”IEEE Transactions on Image Processing, vol. 20,pp. 2378-2386, 2011.

J. Suckling, J. Parker, D.R. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S.L. Kok, P. Taylor, D. Betal, J. Savage, “The Mammographic Image Analysis Society Digital Mammogram Database”,International Congress Series, vol. 1069,pp. 375-378, 1994.

J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B.V. Ginneken, “Ridge based vessel segmentation in color images of the retina,”IEEE Transactions on Medical Imaging, vol. 23,pp. 501-509, 2004.

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