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.
Highlights
Ultrasound (US) Imaging is a portable, accurate, and pervasive healthcare technology used in medical diagnostics
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
The quality of images measured on the basis of peak signal to noise ratio, structured similarity index, signal to noise ratio, mean square error, mean square root error on different intensities of speckle noise varies from 0.01 to 0.1
Summary
Ultrasound (US) Imaging is a portable, accurate, and pervasive healthcare technology used in medical diagnostics. Its characteristics like low cost, harmlessness, instant results make it more adaptable. It provides realtime pictures of human organs like kidney, gallbladder, stomach, liver, and many more. Speckle noise, which is multiplicative in nature reduces fine details and limits the contrast resolution in an image This limits the detection of small, low contrast lesions in the body that restrict the proper diagnostics of body tissues.
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