Abstract
Speckle noise removal plays an important role in ultrasound image diagnosis. Existing speckle noise removal techniques have drawbacks such as lose of edge information, texture information and inability to remove low frequency noise. To overcome these issues, a new discrete shearlet transform (DST) with nonlinear diffusion is proposed in this paper. DST comprises of localization, directionality and multiscale features which are crucial for ultrasound despeckling. Adaptive thresholding and nonlinear diffusion are combined with DST to remove both high frequency and low frequency noises so that the superior filtering performance can be achieved. The comparison performance of the proposed method with other despeckling techniques indicates the superiority of this technique.
References
Thakur A, Anand RS (2005) Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction. Digital Signal Process 15(5):455–465
Loupas T, McDicken WN, Allan PL (1989) An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans Circuits Syst 36:129–135
Liu X, Liu J, Xu X, Chun L, Tang J, Deng Y (2011) A robust detail preserving anisotropicdiffusion for speckle reduction in ultrasound images. BMC Genom 12:S14
Fan Z, Yang YM, Koh LM, Yongmin K (2007) Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction. IEEE Trans Med Imaging 26:200–211
Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90:1200–1224
Do MN, Vetterli M (2003) The finite ridgelet transform for image representation. IEEE Trans Image Process 12:16–28
Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11:670–684
Lee J-S (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2:165–168
Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell PAMI 2:165–177
Liu S, Wei J, Feng B, Lu W, Denby B, Fang Q, Dang J (2012) An anisotropic diffusion filter for reducing speckle noise of ultrasound images based on separability. In: Signal and information processing association annual summit and conference (APSIPA ASC), 2012 Asia-Pacific, IEEE, pp 1–4
Anand CS, Sahambi JS (2010) Wavelet domain non-linear filtering for MRI denoising. Magn Reson Imaging 28:842–861
Roy S, Sinha N, Sen A (2011) Fuzzy soft thresholding based hybrid denoising model. In: Nagamalai D, Renault E, Dhanuskodi M (eds) Advances in digital imageprocessing and information technology. Springer, Berlin, pp 1–10
Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14:2091–2106
Easley G, Labate D, Lim W-Q (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25:25–46
Yongjian Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11:1260–1270
Donoho DL (1995) Denoising by soft-thresholding. IEEE Trans Inf Theory 41:613–627
Finn S, Glavin M, Jones E (2011) Echocardiographic speckle reduction compari-son. IEEE Trans Ultrason Ferroelectr Freq Control 58(1):82–101. https://doi.org/10.1109/TUFFc.2011.1776
Zhou ZF, Shui PL (2007) Contourlet based image denoising algorithm using directional windows. Electron Lett 43(2):92–93
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Ahmed, L.J. Discrete Shearlet Transform Based Speckle Noise Removal in Ultrasound Images. Natl. Acad. Sci. Lett. 41, 91–95 (2018). https://doi.org/10.1007/s40009-018-0620-7
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DOI: https://doi.org/10.1007/s40009-018-0620-7