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HOU Xin, LI Jianwu, LU Yao, DONG Zhengchao. Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising[J]. Chinese Journal of Electronics, 2014, 23(4): 735-741.
Citation: HOU Xin, LI Jianwu, LU Yao, DONG Zhengchao. Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising[J]. Chinese Journal of Electronics, 2014, 23(4): 735-741.
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Improving Nonlocal Means Method via a No-reference Image Content Metric for MRI Denoising

  • 1.

    Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;

  • 2.

    Brain Imaging Lab, Columbia University, New York, NY 10032, United States;

  • 3.

    New York State Psychiatric Institute, New York, NY 10032, United States

Funds: This work is supported by the National Natural Science Foundation of China (No.61271374, No.61273273) and the Beijing Natural Science Foundation (No.4122068).
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  • Received Date: August 31, 2014
  • Revised Date: May 31, 2014
  • Published Date: October 04, 2014
    Graphical Abstract
    • Abstract
  • The patch matching of the traditional Nonlocal means (NLM) filter mainly depends on structure similarity and cannot adapt to the patch rotation or mirroring transformation. Therefore, designing a measure with rotationally invariant similarity is of significant importance for improving the effectiveness of patch comparison of NLM. This paper proposes to apply a no-reference image content metric with the rotation-invariance to NLM for denoising Magnetic resonance (MR) images. The metric measures quantitatively the content of a patch in an image, including sharpness, contrast, and geometric features such as textures and edges. The metric values for every patch are computed and added into the Gaussian matching kernel of NLM so as to effectively perform patch matching. The main advantage of the proposed method is that it does not need to rotate patches in different orientations during patch matching. Experimental results show that the proposed method is superior to the traditional NLM, the state-of-the-art method Block-matching and 3-D (BM3D) filtering and the Unbiased NLM (UNLM) for MRI denoising.
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  • C. Liu, R. Szeliski, S.B. Kang, et al, Automatic estimation and removal of noise from a single image, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.30, No.2, pp.299-314, 2008.
    R.D. Nowak, Wavelet-based Rician noise removal for magnetic resonance imaging, IEEE Transactions on Image Processing, Vol.8, No.10, pp.1408-1419, 1999.
    P. Bao and L. Zhang, Noise reduction for magnetic resonance image via adaptive multiscale products thresholding, IEEE Transactions on Medical Imaging, Vol.22, No.9, pp.1089-1099, 2003.
    C.S. Anand and J.S. Sahambi, Wavelet domain non-linear filtering for MRI denoising, Magnetic Resonance Imaging, Vol.28, No.6, pp.842-861, 2010.
    C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, Proc. of the International Conference on Computer Vision (ICCV), Bombay, India, pp.839-846, 1998.
    P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machines Intelligence, Vol.12, No.7, pp.629-639, 1990.
    W. G. Zhang, S. Wang, F. Liu, X. Gao and L. Jiao, Image denoising using bandelets and hidden Markov tree model, Chinese Journal of Electronics, Vol.19, No.4, pp. 646-650, 2010.
    L.I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D:Nonlinear Phenomena, Vol.60, pp.259-268, 1992.
    J. Bai and X.C. Feng, Image denoising and decomposition using non-convex functional, Chinese Journal of Electronics, Vol.21, No.1, pp.102-106, 2012.
    A. Buades, B. Coll and J.M. Morel, A non-local algorithm for image denoising, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, California, USA, Vol.2, pp.60-65, 2005.
    N. He and K. Lu, A non local feature-preserving strategy for image denoising, Chinese Journal of Electronics, Vol.21, No.4, pp.651-656, 2012.
    Z. Ji, Q. Chen, Q.S. Sun and D.S. Xia, A moment-based nonlocal-means algorithm for image denoising, Information Processing Letters, Vol.109, No.23, pp.1238-1244, 2009.
    J.V. Manjón, P. Coupé, A. Buades, D.L. Collins and M. Robles, New methods for MRI denoising based on sparseness and self-similarity, Medical Image Analysis, Vol.16, No.1, pp.18-27, 2012.
    Y. Lou, P. Favaro, S. Soatto and A. Bertozzi, Nonlocal similarity image filtering, 15th International Conference on Image Analysis and Processing (ICIAP), Vietri sul Mare, Italy, pp.62-71, 2009.
    D.G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, Vol.60, No.2, pp.91-110, 2004.
    W.F. Sun, Y.H. Peng and W.L. Hwang. Modified similarity metric for non-local means algorithm, Electronics Letters, Vol.45, No.25, pp.1307-1309, 2009.
    T. Thaipanich and C.C.J. Kuo, An adaptive nonlocal means scheme for medical image denoising, Proc. SPIE 7623, Medical Imaging 2010: Image Processing, San Diego, California, USA, pp.76230M-1-12, 2010.
    O. Kleinschmidt, T. Brox and D. Cremers, Nonlocal texture filtering with efficient tree structures and invariant patch similarity measures, Proc. International Workshop on Local and Non-local Approximation in Image Processing, Lausanne, Switzerland: IEEE SP/CAS Chapter in Finland, pp.103-113, 2008.
    S. Zimmer, S. Didas and J. Weickert, A rotationally invariant block matching strategy improving image denoising with non-local means, Proc. International Workshop on Local and Non-local Approximation in Image Processing, Lausanne, Switzerland: IEEE SP/CAS Chapter in Finland, pp.135-142, 2008.
    S. Grewenig, S. Zimmer and J. Weickert, Rotationally invariant similarity measures for nonlocal image denoising, Journal of Visual Communication and Image Representation, Vol.22, No.2, pp.117-130, 2011.
    R. Yan, L. Shao, S.D. Cvetkovi? and J. Klijn, Improved nonlocal means based on pre-classification and invariant block matching, Journal of Display Technology, Vol.8, No.4, pp.212-218, 2012.
    J.V. Manjón, J. Carbonell-Caballero, J.J. Lull, G. García-Martí, L. Martí-Bonmatí and M. Robles, MRI denoising using non local means, Medical Image Analysis, Vol.12, No.4, pp.514-523, 2008.
    X. Zhu, P. Milanfar, A no-reference image content metric and its application to denoising, 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, pp.1145-1148, 2010.
    X. Zhu, P. Milanfar, Automatic parameter selection for denoising algorithms using a no-reference measure of image content, IEEE Transactions on Image Processing, Vol.19, No.12, pp.3116-3132, 2010.
    Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, Vol.13, No.4, pp.600-612, 2004.
    J. Huang, S. Zhang and D. Metaxas, Efficient MR image reconstruction for compressed MR imaging, Medical Image Analysis, Vol.15, No.5, pp.670-679, 2011.
    K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, Vol.16, No.8, pp.2080-2095, 2007.
    E. López-Rubio and M.N. Florentín-Núñez, Kernel regression based feature extraction for 3D MR image denoising, Medical Image Analysis, Vol.15, No.4, pp.498-513, 2011.
    A. Pizurica, W. Philips, I. Lemahieu and M. Acheroy, A versatile wavelet domain noise filtration technique for medical imaging, IEEE Transactions on Medical Imaging, Vol.22, No.3, pp.323-331, 2003.
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