A New Ultrasound Speckle Reduction Algorithm Based on Superpixel Segmentation and Detail Compensation

Speckle is a kind of noise commonly found in ultrasound images (UIs). Although traditional local operation-based methods, such as bilateral filtering, perform well in de-noising normal natural images with suitable parameters, these methods may break local correlations and, hence, their performance will be highly degraded when applied to UIs with high levels of speckle noise. In this work, we propose a new method, based on superpixel segmentation and detail compensation, to reduce UI speckle noise. In particular, considering that superpixel segmentation has the advantage of adhering accurately to the boundaries of objects or local structures, we propose a superpixel version of bilateral filtering to better protect the local structure during de-noising. Additionally, a human visual system (HVS)-inspired strategy for spatial compensation is introduced, in order to recover sophisticated edges as much as possible while weakening the high-frequency noise. Experiments on synthetic images and real UIs of different organs show that, compared to other methods, the proposed strategy can reduce ultrasound speckle noise more effectively.