Evaluating the effect of various speckle reduction filters on ultrasound liver cancer images

The main goal of Ultrasound (US) image preprocessing is to reduce noise of an image. It helps consecutive stages of image analysis like classifications or segmentation of liver cancers to differentiate easier and efficiently. In pre-processing stage filtering is the key process used for reducing signal depended noise, so called speckle. The optimal filter model has the main objective of reducing speckle noise by enhancing contrast, smoothing and sharpening of the image signal. Several noise filters are introduced for different capacities and purposes with its own advantages and disadvantages. This paper describes the evaluation and performance analysis of five image filtering techniques, namely Kuan, Frost, Mean, Median and Speckle reducing anisotropic diffusion filter (SRAD) from the spatial filtering process for liver US data. An application of US hepatic liver cancer image was chosen and selected denoising algorithms are applied to estimate the impact on the US speckle image signal. Experiments are investigated based on Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity (MSSIM) and Mean square error (MSE). The result shows that SRAD filter performs better than other denoising filters with a PSNR=31.11 dB, MSE=31.07, MSSIM=0.895.