Affiliations: [1] Department of Mathematics, Faculty of Sciences, University of Mohaghegh Ardabili, Ardabil, Iran | [2] Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Tabriz, Tabriz, Iran. E-mail: [email protected]
Correspondence:
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Corresponding author.
Abstract: Medical Ultrasound is a diagnostic imaging technique based on the application of ultrasound in various branches of medical sciences. It can facilitate the observation of structures of internal body, such as tendons, muscles, vessels and internal organs such as male and female reproductive system. However, these images usually degrade by a special kind of multiplicative noise called speckle. The main effects of speckle noise in the ultrasound images appear in the edges and fine details which lead to reduce their resolution and consequently make difficulties in medical diagnosing. Therefore, reducing of speckle noise seriously plays an important role in image diagnosing. Among the various methods that have been proposed to reduce the speckle noise, there exists a class of approaches that firstly convert multiplicative speckle noise into additive noise via log-transform and secondly perform the despeckling process via a directional filter. Usually, the additive noises are mutually uncorrelated and obey a Gaussian distribution. On the other hand, non-subsampled shearlet transform (NSST), as a multi scale method, is one of the effective methods in image processing, specially, denoising. Since NSST is shift invariant, it diminishes the effect of pseudo-Gibbs phenomena in the denoising. In this paper, we describe a simple image despeckling algorithm which combines the log-transform as a pre-processing step with the non-subsampled shearlet transform for strong numerical and visual performance on a broad class of images. To illustrate the efficiency of the proposed approach, it is applied on a sample image and two real ultrasound images. Numerical results illustrate that the proposed approach can obtain better performance in term of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index rather than existing state-of-the-art methods.
