A Novel Method for Despeckling of Ultrasound Images Using Cellular Automata-Based Despeckling Filter

A Novel Method for Despeckling of Ultrasound Images Using Cellular Automata-Based Despeckling Filter

Ankur Bhardwaj, Sanmukh Kaur, Anand Prakash Shukla, Manoj Kumar Shukla
Copyright: © 2021 |Pages: 20
DOI: 10.4018/IJEHMC.20210901.oa2
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Abstract

Ultrasound images have an inherent property termed as speckle noise that is the outcome of interference between incident and reflected ultrasound waves which reduce image resolution and contrast and could lead to improper diagnosis of any disease. In different approaches for reducing the speckle noise, there exists a class of filters that convert multiplicative noise into additive noise by using algorithmic functions. The current study proposes a cellular automata-based despeckling filter (CABDF) that implements a local spatial filtering framework for the restoration of the noisy image. In the proposed CABDF filter, a dual transition function has been designed which emphasizes the calculation of nearby weighted separation whose loads originate from the CABDF filtered image, including spatial separation, extend inconsistency, and statistical dispersion. The proposed filter found efficient both in terms of filtering and restoration of the original structure of the ultrasound images.
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Background

In the earlier decades, different algorithms have been introduced for speckle denoising including local spatial filters described by Guo et.al (2009), Gabriel et.al (2014) explained anisotropic diffusion (AD) filters, Guo et.al (2011) discussed non-local means (NLM) filters. Jose and Mario (2010) explained the total variation (TV) methods for filtering. Charles Alban et.al (2017) enhanced the previously existing homomorphic approaches. Homomorphic filtering method transforms multiplicative noise into additive one by use of the logarithmic function. Lee Filter (Jongsen Lee,1980) used the least square approach from the computed values of the window. Kuan (Darwin T Kuan et. al,1985) described a filter known as the Kuan filter which employed wavelet-based channels that have been broadly utilized as a result of its versatile behavior.

Achim et. al., (2001) utilized the overwhelmingly followed alpha-stable distribution to show sub-band disintegrations and planned a Bayesian processor to evacuate speckle. Two newly additive gaussian filters named block-matching and three-dimensional filtering (BM3D) (Davoc et. al,2007) and dual-domain image denoising (DDID) (Knaus and Zwicker,2013) work in a homomorphic manner for despeckling. Khare introduced a speckle noise contraction method based on Daubechies wavelet transformation that recognizes the solid edges and segments of complex wavelet coefficients (Khare et al.,2010).

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