access icon free Bayesian denoising of ultrasound images using heavy-tailed Levy distribution

Ultrasound images often exhibit poor signal to noise ratio when compared with optical images, because of the presence multiplicative speckle noise. Speckle suppression is often carried out as a pre-processing step to aid in diagnosis using ultrasound images. In this study, dual tree complex wavelet transform-based Levy Shrink algorithm is proposed for denoising ultrasound images. The coefficients in each wavelet subband are modelled using a heavy tailed Levy distribution. The scale parameters of the Levy distribution are estimated using fractional moments. Within this framework, a Bayesian estimator is employed to denoise ultrasound images. The proposed Levy Shrink algorithm is verified using evaluation parameters such as peak signal to noise ratio, mean structural similarity index, correlation coefficient and equivalent number of looks. The efficiency of the proposed denoising algorithm is justified by conducting extensive experiments on real as well as simulated ultrasound images.

Inspec keywords: statistical distributions; Bayes methods; image denoising; wavelet transforms; medical image processing; ultrasonic imaging; biomedical ultrasonics

Other keywords: ultrasound image denoising; Bayesian estimator; speckle suppression; heavy tailed Levy distribution; Bayesian denoising; Levy Shrink algorithm; dual tree complex wavelet transform

Subjects: Sonic and ultrasonic radiation (biomedical imaging/measurement); Sonic and ultrasonic applications; Function theory, analysis; Integral transforms; Biology and medical computing; Other topics in statistics; Sonic and ultrasonic radiation (medical uses); Other topics in statistics; Computer vision and image processing techniques; Probability theory, stochastic processes, and statistics; Integral transforms; Patient diagnostic methods and instrumentation

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