Abstract
Speckle reduction is a crucial prerequisite of many computer-aided ultrasound diagnosis and treatment systems. However, most of existing speckle reduction filters concentrate the blurring near features and introduced the hole artifacts, making the subsequent processing procedures complicated. Optimization-based methods can globally distribute such blurring, leading to better feature preservation. Motivated by this, we propose a novel optimization framework based on \(L_{0}\) minimization for feature preserving ultrasound speckle reduction. We observed that the GAP, which integrates gradient and phase information, is extremely sparser in despeckled images than in speckled images. Based on this observation, we propose the \(L_{0}\) minimization framework to remove speckle noise and simultaneously preserve features in ultrasound images. It seeks for the \(L_{0}\) sparsity of the \(\textit{GAP}\) values, and such sparsity is achieved by reducing small \(\textit{GAP}\) values to zero in an iterative manner. Since features have larger \(\textit{GAP}\) magnitudes than speckle noise, the proposed \(L_{0}\) minimization is capable of effectively suppressing the speckle noise. Meanwhile, the rest of \(\textit{GAP}\) values corresponding to prominent features are kept unchanged, leading to better preservation of those features. In addition, we propose an efficient and robust numerical scheme to transform the original intractable \(L_{0}\) minimization into several sub-optimizations, from which we can quickly find their closed-form solutions. Experiments on synthetic and clinical ultrasound images demonstrate that our approach outperforms other state-of-the-art despeckling methods in terms of noise removal and feature preservation.
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Vegas-Sanchez-Ferrero, G., Aja-Fernandez, S., Martin-Fernandez, M., Frangi, A.F., Palencia, C.: Probabilistic-driven oriented speckle reducing anisotropic diffusion with application to cardiac ultrasonic images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 518–525. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15705-9_63
Flores, W.G., de Albuquerque Pereira, W.C., Infantosi, A.F.C.: Breast ultrasound despeckling using anisotropic diffusion guided by texture descriptors. Ultrasound Med. Biol. 40, 2609–2621 (2014)
Wang, B., Cao, T., Dai, Y., Liu, D.C.: Ultrasound speckle reduction via super resolution and nonlinear diffusion. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5996, pp. 130–139. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12297-2_13
Cheng, H., Shan, J., Ju, W., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn. 43, 299–317 (2010)
Esakkirajan, S., Vimalraj, C.T., Muhammed, R., Subramanian, G.: Adaptive wavelet packet-based de-speckling of ultrasound images with bilateral filter. Ultrasound Med. Biol. 39, 2463–2476 (2013)
Balocco, S., Gatta, C., Pujol, O., Mauri, J., Radeva, P.: SRBF: speckle reducing bilateral filtering. Ultrasound Med. Biol. 36, 1353–1363 (2010)
Coupé, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans. Image Process. 18, 2221–2229 (2009)
Yang, J., Fan, J., Ai, D., Wang, X., Zheng, Y., Tang, S., Wang, Y.: Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. IEEE Trans. Image Process. 195, 88–95 (2016)
Tay, P.C., Garson, C.D., Acton, S.T., Hossack, J.A.: Ultrasound despeckling for contrast enhancement. IEEE Trans. Image Process. 19, 1847–1860 (2010)
Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.N.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23, 5638–5653 (2014)
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via \(L_{0}\) gradient minimization. IEEE Trans. Image Process. 30, 174 (2011)
Li, Z., Zheng, J., Zhu, Z., Yao, W., Wu, S.: Weighted guided image filtering. IEEE Trans. Image Process. 24, 120–129 (2015)
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11, 1260–1270 (2002)
Belaid, A., Boukerroui, D., Maingourd, Y., Lerallut, J.F.: Phase-based level set segmentation of ultrasound images. IEEE Trans. Image Process. 15, 138–147 (2011)
Khare, A., Khare, M., Jeong, Y., Kim, H., Jeon, M.: Despeckling of medical ultrasound images using daubechies complex wavelet transform. Sig. Process. 90, 428–439 (2010)
Cardoso, F.M., Matsumoto, M.M., Furuie, S.S.: Edge-preserving speckle texture removal by interference-based speckle filtering followed by anisotropic diffusion. Ultrasound Med. Biol. 38, 1414–1428 (2012)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: CVPR, vol. 2, pp. 60–65 (2005)
Yu, J., Tan, J., Wang, Y.: Ultrasound speckle reduction by a Susan-controlled anisotropic diffusion method. Pattern Recogn. 43, 3083–3092 (2010)
Morrone, M.C., Ross, J., Burr, D.C., Owens, R.: Mach bands are phase dependent. Nature 324, 250–253 (1986)
Kovesi, P.: Symmetry and asymmetry from local phase. In: Tenth Australian Joint Conference on Artificial Intelligence, vol. 190. Citeseer (1997)
Kovesi, P.: Image features from phase congruency. Nature 1, 1–26 (1999)
Boukerroui, D., Noble, J.A., Brady, M.: On the choice of band-pass quadrature filters. Nature 21, 53–80 (2004)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Nature 30, 117–156 (1998)
Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Process. 4, 932–946 (1995)
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: Advances in Neural Information Processing Systems, pp. 1033–1041 (2009)
Daubechies, I., DeVore, R., Fornasier, M., Güntürk, C.S.: Iteratively reweighted least squares minimization for sparse recovery. Commun. Pure Appl. Math. 63, 1–38 (2010)
Yi, S., Wang, X., Lu, C., Jia, J.: \(L_{0}\) regularized stationary time estimation for crowd group analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2211–2218 (2014)
Massoptier, L., Casciaro, S.: A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans. Eur. Radiol. 18, 1658–1665 (2008)
Acknowledgement
We thank reviewers for the various valuable comments. This work was supported by the Hong Kong Research Grants Council General Research Fund (Project No. CUHK 14202514), Hong Kong Innovation and Technology Fund for Hong Kong-Shenzhen Innovation Circle Funding Program (No. GHP/002/13SZ and SGLH20131010151755080), the Natural Science Foundation of Guangdong Province (Project No. 2014A030310381), the National Natural Science Foundation of China (Project No. 61233012 and 61305097), the Research and Development Project of Guangdong Key Laboratory of Robotics and Intelligent Systems (Grant No. ZDSYS20140509174140672), and Shenzhen Basic Research Program (Project No. JCYJ20150525092940988).
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Zhu, L. et al. (2017). Ultrasound Speckle Reduction via \(L_{0}\) Minimization. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_4
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