Implementation of Adaptive Wavelet Thresholding and Nonlocal Means for Medical Image Enhancement for Noise Reduction

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-24 Number-1
Year of Publication : 2015
Authors : Prerna Mamgain, Sachin Chaudhary
  10.14445/22312803/IJCTT-V24P105

MLA

Prerna Mamgain, Sachin Chaudhary "Implementation of Adaptive Wavelet Thresholding and Nonlocal Means for Medical Image Enhancement for Noise Reduction". International Journal of Computer Trends and Technology (IJCTT) V24(1):23-28, June 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Images are most widely used for radiological diagnosis in medical examinations. The presence of artifacts and noise in images causes the difficulty in medical diagnosis. The noises are generally occurred and corrupt an image during its acquisition or transmission. Image denoising is one of the popular methods with an aim of noise reduction to retain images quality. In this paper, Wavelet based noise reduction technique is proposed to improve image quality where thresholding and Non-local means algorithm are applied. The Noisy medical image is decomposed using DWT, where approximation part is filtered using Nonlocal means filter and detail parts are filtered by the thresholding. By using the level dependent, the wavelet coefficients are calculated using optimal linear interpolation shrinkage function. Denoised image is acquired using inverse DWT. The value of the peak signal to noise ratio (PSNR) is used as the measure of image visual quality.

References
[1] M. K. Kalra, M. M. Maher and T. L. Toth, “Strategies for CT radiation dose optimization,” Journal of Radiology, vol. 230, no.3, pp. 619-628, 2004.
[2] S. G. Chang, B. Yu and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. on Image Proc., vol. 9, no. 9, pp. 1532-1546, 2000.
[3] S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. on Pattern Anal. Mach. Intell., vol. 11, pp. 674-693, no. 7, 1989.
[4] M. Nasri and H. Nezamabadi-Pour, “Image denoising in the wavelet domain using a new adaptive thresholding function,” Neurocomputing, vol. 72, nos. 4–6, pp. 1012–1025, 2009.
[5] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation via wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994.
[6] Sankur B. and Sezginb M. “Image Thresholding Techniques: a Survey over Categories,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-165 2004.
[7] K. Li and R. Zhang, “Multiscale wiener filtering method for low-dose CT images,” IEEE Biomedical Engineering and Informatics, New York pp. 428– 431, 2010.
[8] Cheng-Ting Shih, Shu-Jun Chang, Yan-lin Liu and Jay Wu " Noise reduction of low-dose computed tomography using the multi-resolution total variation minimization algorithm, " Proc. SPIE, Physics of Medical Imaging, Vol. 8668, 2013.
[9] A. Borsdorf, S. Kappler and R. Raupach, “Analytic noise propagation for anisotriopic denoising of CT images,” IEEE Nucl Sci Symp Conf Rec, pp. 5335– 5338, 2008.
[10] J. Saeedi and M. H. Moradi, “A new wavelet-based fuzzy single and multi-channel image denoising,” Image Vis. Comput., vol. 28, no. 12, pp. 1611– 1623, 2010.
[11] L. Shui, Z. F. Zhou, and J. X. Li, “Image denoising algorithm via best wavelet packet base using Wiener cost function,” IET Image Process., vol. 1, no. 3, pp. 311–318, 2007.
[12] Yunhong Li, Xin Yi, Jian Xu and Yuxuan Li, “Wavelet packet denoising algorithm based on correctional wiener filtering,” Journal of Information and Computational Science, vol. 10, no. 9, pp. 2711-2718, 2013.
[13] A. Fathi and A. R. Naghsh-Nilchi, “Efficient image denoising method based on a new adaptive wavelet packet thresholding function,” IEEE Trans Image Process, vol. 21, no. 9, pp. 3981-90, 2012.
[14] A. Buades, B. Coll, J.M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul. 4 (2005) 490–530.
[15] J.V. Manjon, M. Robles, N.A. Thacker, Multispectral MRI de-noising using nonlocal means, Med. Image Understand. Anal. (MIUA) (2007) 41–46.
[16] J.V. Manjón, J. Carbonell-Caballero, J.J. Lull, G. García-Martí, L. Martí-Bonmatí, M. Robles, MRI denoising using non-local means, Med. Image Anal. 12 (2008) 514–523.
[17] P. Coupe, P. Yger, C. Barillot, Fast non local means denoising for MR images, in: Proceedings at the 9th International Conference on Medical Image Computing and Computer assisted Intervention (MICCAI), Copenhagen, 2006, pp. 33–40.
[18] P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann, C. Barillot, An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images, IEEE Trans. Med. Imaging 27 (2008) 425–441.
[19] P. Coupe, P. Hellier, S. Prima, C. Kervrann, C. Barillot, 3D wavelet subbands mixing for image denoising, Int. J. Biomed. Imaging (2008), http://dx.doi.org/10.1155/2008/590183, Article ID 590183.
[20] N. Wiest-Daesslé, S. Prima, P. Coupé, S.P. Morrissey, C. Barillot, Nonlocal means variants for denoising of diffusion-weighted and diffusion tensor MRI, Med. Image Comput. Comput. Assist. Interv. (2007) 344–351.
[21] Rupinderpal Kaur, Rajneet Kaur"Image Denoising Based on Wavelet Technique using Thresholding forMedical Images"International Journal of Computer Trends and Technology (IJCTT),V4(8) August Issue 2013.

Keywords
DWT, PSNR, denoising, thresholding, decomposition.