Abstract
•Applied six de-noising filters (i.e., Mean, Median, Kuan, Lee, Frost and adaptive Homomorphic) on original and noisy images. These filters are commonly used for speckle noise reduction to demonstrate the filter efficiency across a range of speckle-noise degradation variances.•Results were analyzed based on four image quality metrics (i.e., peak signal to noise ratio (PSNR), root mean square error (RMSE), speckle suppression index (SSI), and standard deviation to mean ratio (STM)).•The metrics were further analyzed by using the statistical tests (i.e., one-way analysis of variance (ANOVA) and Tukey’s post hoc test).
In the modern-days diagnostics, ultrasound is considered a significant non-invasive imaging technique. However, ultrasound images are frequently contaminated by multiplicative speckle noise. Speckle noise is produced by constructive and destructive interference between ultrasound waves when the object being measured is smaller than the wavelength of the beam. The removal of speckle noise in ultrasound images is possible by using various filters. The current experimental study is aimed at finding the most suitable de-speckling filters for enhancing ultrasound images. The present study involves 102-abdomen ultrasound images degraded by speckle noise and the analysis of eight de-speckling filters (i.e., Mean, Median, Kuan, Lee, Frost, Adaptive Homomorphic, Wiener and Anisotropic Diffusion) is developed to find the optimum de-speckling filter. The results of the given filters are analyzed using the five quality metrics (i.e., PSNR, RMSE, SSI, STM, and SSIM). The metrics are further investigated by using the statistical tests (i.e., one-way ANOVA and Tukey’s post hoc test) to support the experimental evaluation. Simulation results showed that the Frost and adaptive Homomorphic filters work well for the ultrasound images degraded with higher value of speckle noise. From the proposed research, it is easy to select the optimal filter, which helps doctors to get fine details of the image.