Ultrasound image smoothing based on adaptive and non-adaptive filters

Abstract

Objective: To model adaptive and non-adaptive filters to ensure smooth ultrasound images.
Method: The comparative study was conducted at Al-Yarmouk Teaching Hospital, Al Mustansiriyah University,
Baghdad, Iraq, in 2019, and comprised ultrasound images of kidney (303x208 pixel) and foetus (111x109 pixel).
These images were smoothed based on 8 filters; 1 non-adaptive (median), and 7 adaptive enhanced filters (Gamma,
Wiener, Lee, Frost, Kuan, Adaptive Lee and Adaptive Frost). They were applied to the images by windows measuring
3x3, 5x5, 7x7. The additive noise and the multiplicative noise factor were calculated using histogram to determine
the noise type for each image. Statistical criteria included mean square error, normalised absolute error and signalto-
noise ratio.
Result: The relationship between noise ratio and filter type showed that Wiener was the best filter and the best
sliding window was 3x3. The worst filters were Gamma, EFrost and Kuan.
Conclusion: The relationship between sliding window size and noise ratio for all the smoothing filters clearly
identified the best filter for the type of noise.
Key Words: Kidney, Fetus, Ultrasonography, Smoothing