Abstract
In this paper a new method of multiplicative noise reduction in ultrasound images is proposed. The novel technique is a modification of the bilateral denosing scheme, which takes into account the similarity of pixels and their spatial distance. The filter output is calculated as a weighted average of the pixels which are in the neighborhood relation with the center of the filtering window, and the weights are functions of the minimal connection costs between surounding pixels. Experimental results show that the proposed method yields significantly better results than the other techniques in case of ultrasound images contaminated by medium and strong multiplicative noise disturbances.
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References
Michailovich, O., Tannenbaum, A.: Despeckling of medical ultrasound images. IEEE Trans. Ultrason., Ferroelect., Freq. Contr. 53, 64–78 (2006)
Sarode, M., Deshmukh, P.: Reduction of speckle noise and image enhancement of images using filtering technique. International Journal of Advancements in Technology 2(1), 30–38 (2011)
Loizou, C.P., Pattichis, C.S.: Despeckle Filtering Algorithms and Software for Ultrasound Imaging. Synthesis Lectures on Algorithms and Software in Engineering. Morgan and Claypool Publishers (2008)
Rosa, R., Monteiro, F.C.: Speckle ultrasound image filtering: Performance analysis and comparison. In: Computational Vision and Medical Image Processing IV: VIPIMAGE (2013)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, ICCV 1998, p. 839. IEEE Computer Society, Washington, DC (1998)
Malik, K., Smolka, B.: Improved bilateral filtering scheme for noise removal in color images. In: The International Conference on Informatics and Applications (ICIA 2012) (2012)
Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. International Journal Computer Vision 81(1), 24–52 (2009)
Kuan, D.T., Sawchuk, A., Strand, T.C., Chavel, P.: Adaptive restoration of images with speckle. IEEE Transactions on Speckle ultrasound image Acoustics, Speech and Signal Processing 35(3), 373–383 (1987)
Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. on Pattern Analysis and Machine Intelligence 4(2), 157–166 (1982)
Jin, F., Fieguth, P., Winger, L., Jernigan, E.: Adaptive wiener filtering of noisy images and image sequences. In: International Conference on Image Processing, ICIP 2003, vol. 3, pp. 349–352 (2003)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)
Zhi, X., Wang, T.: An anisotropic diffusion filter for ultrasonic speckle reduction. In: 5th International Conference on Visual Information Engineering, VIE 2008, pp. 327–330 (2008)
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 11(11), 1260–1270 (2002)
Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)
Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 1200–1224 (1995)
Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing 9(9), 1532–1546 (2000)
Prakash, K.B., Babu, R.V., VenuGopal, B.: Image independent filter for removal of speckle noise. International Journal of Computer Science Issues 8(5) (2011)
Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Transactions on Communications 43(12), 2959–2965 (1995)
Avcibas, I., Sankur, B., Sayood, K.: Statistical evaluation of image quality measures. Journal of Electronic Imaging 11, 206–223 (2002)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters (3), 81–84 (2002)
Aja-Fernandez, S., Estepar, R.S.J., Alberola-Lopez, C., Westin, C.-F.: Image quality assessment based on local variance. In: 28th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBS 2006, pp. 4815–4818. IEEE (2006)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error measurement to structural similarity. IEEE Transactions on Image Processing 13, 600–612 (2004)
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Malik, K., Machala, B., Smolka, B. (2014). Novel Approach to Noise Reduction in Ultrasound Images Based on Geodesic Paths. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_49
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DOI: https://doi.org/10.1007/978-3-319-11331-9_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11330-2
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