Abstract
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.
Highlights
Ultrasound images (UI) are becoming more and more popular in clinical diagnosis due to their economical and practical advantages
We proposed a speckle noise reduction method for ultrasound images (UIs), based on the strategies of superpixel segmentation and spatial compensation
The technique of superpixel segmentation was used to sparsely code the input image, which can be regarded as a superpixel version of bilateral filtering
Summary
Ultrasound images (UI) are becoming more and more popular in clinical diagnosis due to their economical and practical advantages. An extensive review of despeckling methods using various transformation techniques can be found in [5] Along another line, the commonly used spatial domain methods [6,7,8,9] use local statistics to represent the information of the recovered images. The commonly used spatial domain methods [6,7,8,9] use local statistics to represent the information of the recovered images These methods are often based on the multiplicative speckle model. Another group of sparse representation methods—namely non-local methods—has been proposed, in order to break the local dependence These methods use the property of information redundancy among similar patches to reduce the noise [12,13].
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