CVPR 2017

A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction

Lei Zhu¹ Chi-Wing Fu^1,3 Michael S. Brown² Pheng-Ann Heng^1,3

¹The Chinese Univeristy of Hong Kong ²York Univeristy

³Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology,

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Despeckled results of our method on ultrasound images of four different tissue regions. First row: original images; second row: despeckled images; and third row: removed speckle noise component.

Abstract

`Speckle' refers to the granular patterns that occur in ultrasound images due to wave interference. Speckle removal can greatly improve the visibility of the underlying structures in an ultrasound image and enhance subsequent post-processing. We present a novel framework for speckle removal based on low-rank non-local filtering. Our approach works by first computing a guidance image that assists in the selection of candidate patches for non-local filtering in the face of significant speckles. The candidate patches are further refined using a low-rank minimization estimated using a truncated weighted nuclear norm (TWNN) and structured sparsity. We show that the proposed filtering framework produces results that outperform state-of-the-art methods both qualitatively and quantitatively. This framework also provides better segmentation results when used for pre-processing ultrasound images.

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Bibtex

inproceedings {zhu2017non,

title = {A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction,

author = {Lei Zhu, Chi-Wing Fu, Michael S. Brown, and Pheng-Ann Heng},

booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

year = {2017}

}

Last update: March 29, 2017