Abstract
This paper presents a new approach to multiplicative noise removal in ultrasound images. The proposed algorithm utilizes concept of digital paths created on the image grid presented in [14] adapted to the needs of multiplicative noise reduction. Digital Paths are used to determine the filter weights taking into account the structures present in the image. Method of creating path is crucial for the efficiency and speed of the filter. The new approach uses special type of digital paths based on so called Escaping Path Model created with extended neighborhood system. The experiments confirmed that the proposed algorithm achieves a comparable results with the existing state of the art denoising schemes in suppressing multiplicative noise in ultrasound images.
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Achim, A., Bezerianos, A., Tsakalides, P.: Ultrasound image denoising via maximum a posteriori estimation of wavelet coefficients. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 2553–2556 (2001)
Buades, A., Coll, B., Morel, J.-M.: Non-Local Means Denoising. Image Processing On Line, 1 (2011). doi:10.5201/ipol.2011.bcm_nlm
Coupe, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans. Image Proc. 18(10), 2221–2229 (2009)
Cuisenaire, O.: Distance transformations: fast algorithms and applications to medical image processing. Ph.D. Thesis, Universite Catholique de Louvain, October 1999
Deledalle, C.-A., Denis, L., Tupin, F.: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Proc. 18(12), 2661–2672 (2009)
Hacini, M., Hachouf, F., Djemal, K.: A new speckle filtering method for ultrasound images based on a weighted multiplicative total variation. Sig. Process. 103, 214–229 (2013)
Jensen, J.A., Svendsen, N.B.: Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 39(2), 262–267 (1992)
Krissian, K., Kikinis, R., Westin, C. F., Vosburgh, K.: Speckle-constrained filtering of ultrasound images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 547–552 (2005)
Latifoglu, F.: A novel approach to speckle noise filtering based on artificial bee colony algorithm: An ultrasound image application. Comput. Methods Programs Biomed. 111(3), 561–569 (2013)
Loizou, C.P., Theofanous, C., Pantziaris, M., Kasparis, T.: Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery. Comput. Methods Programs Biomed. 114(1), 109–124 (2014)
Loupas, T., McDicken, W.N., Allan, P.L.: An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. Circuits Syst. 36(1), 129–135 (1989)
Von Neumann, J.: Theory of Self-Reproducing Automata. University of Illinois Press, Champaign (1966)
Slabaugh, G., Unal, G., Fang, T., Wels, M.: Ultrasound-specific segmentation via decorrelation and statistical region-based active contours. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 45–53 (2006)
Szczepanski, M., Smolka, B., Plataniotis, K.N., Venetsanopoulos, A.N.: On the geodesic paths approach to color image filtering. Sig. Process. 83(6), 1309–1342 (2003)
Tao, Z., Tagare, H.D., Beaty, J.D.: Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE Trans. Med. Imaging 25(11), 1483–1491 (2006)
Toivanen, P.J.: New geodesic distance transforms for gray scale images. Pattern Recogn. Lett. 17, 437–450 (1996)
Wagner, R.F., Smith, S.W., Sandrik, J.M., Lopez, H.: Statistics of speckle in ultrasound b-scans. IEEE Trans. on Sonics and Ultrasonics 30(3), 156–163 (1983)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Proc. 13(4), 600–612 (2004)
Yue, W., Tracey, B., Natarajan, P., Noonan, J.P.: Probabilistic non-local means. IEEE Signal Process. Lett. 20(8), 763–766 (2013)
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. on Image Proc. 11(11), 1260–1270 (2002)
Acknowledgments
Marek Szczepanski was supported by the Polish National Science Center (NCN) under the Grant: DEC-2012/05/B/ST6/03428. Krystian Radlak was supported by the Norwegian Financial Mechanism 2009-2014 under Project Contract No. Pol-Nor/204256 /16/2013.
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Szczepanski, M., Radlak, K., Popowicz, A. (2015). Escaping Path Approach with Extended Neighborhood for Speckle Noise Reduction. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_21
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DOI: https://doi.org/10.1007/978-3-319-19390-8_21
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