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Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images

Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images

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© The Institution of Engineering and Technology
Published

Ultrasound imaging is widely used for diagnostic purposes among the clinicians. A major problem concerning the ultrasound images is their inherent corruption by the multiplicative speckle noise that hampers the quality of the diagnosis, and reduces the efficiency of the algorithms for automatic image processing. In this paper, we propose a new spatially adaptive wavelet-based method in order to reduce the speckle noise from ultrasound images. A spatially adaptive threshold is introduced for denoising the coefficients of log-transformed ultrasound images. The threshold is obtained from a Bayesian maximum a posteriori estimator that is developed using a symmetric normal inverse Gaussian probability density function (PDF) as a prior for modelling the coefficients of the log-transformed reflectivity. A simple and fast method is provided to estimate the parameters of the prior PDF from the neighbouring coefficients. Extensive simulations are carried out using synthetically speckled and ultrasound images. It is shown that the proposed method outperforms several existing techniques in terms of the signal-to-noise ratio, edge preservation index and structural similarity index and visual quality, and in addition, is able to maintain the diagnostically significant details of ultrasound images.

Inspec keywords: wavelet transforms; medical image processing; image denoising; probability; ultrasonic imaging; Gaussian processes

Other keywords: diagnostic purposes; symmetric normal inverse Gaussian probability density function; structural similarity index; image processing; adaptive wavelet-based method; wavelet domain; signal-to-noise ratio; visual quality; edge preservation index; ultrasound imaging; speckle noise reduction; log-transformed ultrasound images; Bayesian maximum a posteriori estimator; multiplicative speckle noise; spatial adaptive thresholding

Subjects: Sonic and ultrasonic radiation (biomedical imaging/measurement); Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Probability theory, stochastic processes, and statistics; Other topics in statistics; Patient diagnostic methods and instrumentation; Biology and medical computing; Computer vision and image processing techniques; Integral transforms; Other topics in statistics; Optical, image and video signal processing; Integral transforms; Function theory, analysis

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