Wavelet-based statistical approach for speckle reduction in medical ultrasound images |
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Authors: | Email author" target="_blank">S?GuptaEmail author R?C?Chauhan S?C?Sexana |
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Institution: | (1) Department of Computer Science & Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, PB, India;(2) Thapar Institute of Engineering & Technology, Patiala, India |
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Abstract: | A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically
transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band
coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in
the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating
the threshold depend on scale and sub-band data. The threshold was computed by Kσ/σx, where σ and σx were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter.
Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29%
in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these
quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis
in medical image processing. |
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Keywords: | Discrete wavelet transform Speckle reduction Soft thresholding Wiener filter Median filter BayesShrink |
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