Curvelet-Based Bayesian Estimator for Speckle Suppression in Ultrasound Imaging
Ultrasound images are inherently affected by speckle noise, and thus reducing this noise is crucial for successful post-processing. One powerful approach for noise suppression in digital images is Bayesian estimation. In the Bayesian-based despeckling schemes, the choice of suitable statistical models and the development of a shrinkage function for estimation of the noise-free signal are the major concerns. In this paper, a novel curvelet-based Bayesian estimator for speckle removal in ultrasound images is developed. The curvelet coefficients of the degradation model of the noisy ultrasound image are decomposed into two components, namely noise-free signal and signal-dependent noise. The Cauchy and two-sided exponential distributions are assumed to be statistical models for the two components, respectively, and an efficient low-complexity realization of the Bayesian estimator is proposed. The experimental results demonstrate the superiority of the proposed despeckling scheme in achieving significant speckle suppression and preserving image details.
KeywordsUltrasound imaging Curvelet transform Speckle noise Bayesian estimation Statistical modeling
- 4.Sridhar, B., Reddy, K., Prasad, A.: An unsupervisory qualitative image enhancement using adaptive morphological bilateral filter for medical images. Int. J. Comput. Appl. 10(2i), 1 (2014)Google Scholar
- 7.Hiremath, P., Akkasaligar, P.T., Badiger, S.: Speckle reducing contourlet transform for medical ultrasound images. Int. J. Compt. Inf. Eng. 4(4), 284–291 (2010)Google Scholar
- 9.Deng, C., Wang, S., Sun, H., Cao, H.: Multiplicative spread spectrum watermarks detection performance analysis in curvelet domain. In: 2009 International Conference on E-Business and Information System Security (2009)Google Scholar
- 10.Damseh, R.R., Ahmad, M.O.: A low-complexity MMSE Bayesian estimator for suppression of speckle in SAR images. In: 2016 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1002–1005. IEEE (2016)Google Scholar
- 12.Siemens Healthineers. https://www.healthcare.siemens.com/ultrasound. Accessed 06 Jan 2017