COMPARING THE PERFORMANCE OF VARIOUS SPECKLE REDUCTION FILTERS ON ULTRASOUND KIDNEY STONE IMAGES

Abstract

The major problem of ultrasound imaging technique is inheritance of Speckle noise. Speckle is a multiplicative noise that reduces both image contrast and detail resolution, degrades tissue texture, reduces the visibility of small low-contrast lesions and makes continuous structures appear discontinuous, thereby decreasing the quality and reliability of medical ultrasound. It also limits the effective application (e.g. edge detection) of automated computer analysis. As a result, image processing methods for restoration or reduction of speckle noise from ultrasound images has become the predominant step in medical image processing. In this study four de-noising technique such as mean filter, median filter, Gaussian filter and Wiener filter have been developed for de-speckling of ultrasound kidney stone images. Then, the techniques applied were assessed by measuring the image quality (by calculating the MSE and PSNR of the image) and also by applying a level set image segmentation techniques to the output images to know which types of filter produce best quality images. In terms of PSNR and MSE results Wiener filtering eliminates more noise compared to other enhancement techniques and outperformed mean filter, median filter and Gaussian filter by 40.54%, 49.36% and 58.5% respectively on average. On the other hand based on level set segmentation method of the output filtered image, it was also observed that the segmentation of the edge of kidney stone of Wiener filtered achieved low FP area ratio and best SI rates than all other filter.