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Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images
Volume 30, Issue 1 (2019), pp. 1–19
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 September 2017
Accepted
1 November 2018
Published
1 January 2019

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.

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Biographies

Abazari Reza
abazari-r@uma.ac.ir

R. Abazari received the MS degree in applied mathematics from University of Mohaghegh Ardabili, Ardabil, Iran, in 2008 and PhD degree in applied mathematics in 2017 from University of Tabriz, Tabriz, Iran. He is now an adjunct lecturer in applied mathematics at University of Mohaghegh Ardabili since September 2009. He is also an research associate at the Young Researchers and Elite Club, Ardabil, Iran. His research interests include mathematical method in physics, medical sciences and image analysis.

Lakestani Mehrdad

M. Lakestani received the BS degree in applied mathematics from University of Tabriz, Iran, in 1998 and the MSc and PhD degree both in applied mathematics from Amirkabir University of Technology – Tehran Polytechnic, Tehran, Iran, in 2000 and 2005, respectively. Since 2005, he is a professor at University of Tabriz. His research interests include numerical analysis, with special emphasis on wavelet theory, time-frequency analysis and image processing.


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Keywords
ultrasound image discrete shearlet transform non-subsampling log-transform speckle noise

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