Modified non-local means filter for effective speckle reduction in ultrasound images

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, capability of forming real time imaging, and the continuing improvements in image quality. However; the usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the Non Local (NL-) means filter adapted for US images is proposed based on Similarity function depend on specific characteristics of the variance speckle noise in ultrasound images. The proposed method has been compared with Median, Wavelet, Mean and variance local statistics, Geometric, Anisotropic diffusion filtering, and Non ' local means filter using quantitative parameters. From the visual results and image quality evaluation metrics obtained over real images we can conclude that the modified(NL-) means filter can be successfully used for ultrasound image denoising, and performs better results than all other methods while still retaining the structural details and retains the edges and textures very well while removing speckle noise.

[1]  C. Burckhardt Speckle in ultrasound B-mode scans , 1978, IEEE Transactions on Sonics and Ultrasonics.

[2]  R. F. Wagner,et al.  Statistics of Speckle in Ultrasound B-Scans , 1983, IEEE Transactions on Sonics and Ultrasonics.

[3]  Pierrick Coupé,et al.  Nonlocal Means-Based Speckle Filtering for Ultrasound Images , 2009, IEEE Transactions on Image Processing.

[4]  L. J. Busse,et al.  A model based approach to improve the performance of the geometric filtering speckle reduction algorithm , 1995, 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium.

[5]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[6]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[7]  A. Webb,et al.  Introduction to biomedical imaging , 2002 .

[8]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[9]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[10]  Jong-Sen Lee,et al.  Digital image smoothing and the sigma filter , 1983, Comput. Vis. Graph. Image Process..

[11]  Scott T. Acton,et al.  Speckle reducing anisotropic diffusion , 2002, IEEE Trans. Image Process..

[12]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[13]  Keh-Shih Chuang,et al.  A novel image quality index using Moran I statistics. , 2003, Physics in medicine and biology.

[14]  L. P. I︠A︡roslavskiĭ Digital picture processing : an introduction , 1985 .

[15]  D. Sakrison,et al.  On the Role of the Observer and a Distortion Measure in Image Transmission , 1977, IEEE Trans. Commun..

[16]  J. Goodman Some fundamental properties of speckle , 1976 .

[17]  Dhanalakshmi Srinivasan,et al.  A View on Despeckling in Ultrasound Imaging , 2009 .

[18]  Alin Achim,et al.  Novel Bayesian multiscale method for speckle removal in medical ultrasound images , 2001, IEEE Transactions on Medical Imaging.

[19]  Jean-Michel Morel,et al.  Nonlocal Image and Movie Denoising , 2008, International Journal of Computer Vision.

[20]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[22]  Thomas S. Huang,et al.  A fast two-dimensional median filtering algorithm , 1979 .

[23]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Leonid P. Yaroslavsky,et al.  Digital Picture Processing , 1985 .

[25]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).