Original Contribution
Ultrasound Image Despeckling Based on Statistical Similarity

https://doi.org/10.1016/j.ultrasmedbio.2017.05.006Get rights and content
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Abstract

Ultrasound images are affected by the speckle phenomenon, a multiplicative noise that degrades image quality. Several methods for denoising have been proposed in recent years, based on different approaches. The so-called non-local mean is considered the state-of-the-art method; the idea is to find similar patches across the image and exploit them to regularize the image. The method proposed here is in the non-local family, although instead of partitioning the target image in patches, it works pixelwise. The similarity between pixels is evaluated by analyzing their statistical behavior, in particular, by measuring the Kolmogorov–Smirnov distance between their distributions. To make this possible, a stack of acquired images is required. The proposed method has been tested on both simulated and real data sets and compared with other widely adopted techniques. Performance is interesting, with quality parameters and visual inspection confirming such findings.

Key Words

Speckle
Noise reduction
Non-local mean
Spatial filter
Ultrasound images
Image processing

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Conflict of interest disclosure: The author declares that there are no conflicts of interest regarding the publication of this paper. This work received no funding.