Non-local means kernel regression based despeckling of B-mode ultrasound images

Abstract

Medical ultrasound scanning is a widely used diagnostic imaging modality in health-care. Speckle is inherent noise present in ultrasound images reducing the diagnostic accuracy of ultrasound scanning. Speckle noise contributes to high variance between pixels and delineates boundaries of the organs. Effective despeckling involves reducing the variance between pixels corresponding to homogeneous region and to preserve anatomical details simultaneously. Non-Local Means filters are highly successful and produced state of the art results in despeckling ultrasound images. In this paper, we show the effectiveness of Non-Local Means filter with polynomial regression kernel in despeckling ultrasound images. The proposed algorithm is evaluated on software simulated and real time ultrasound images and proved very effective in both despeckling and edge preservation.