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Title: Fast feature-preserving speckle reduction for ultrasound images via phase congruency
Authors: Zhu, L
Wang, W
Qin, J 
Wong, KH
Choi, KS 
Heng, PA
Keywords: Feature asymmetry
Iteratively weighted least squares
Phase congruency
Speckle reduction
Ultrasound images
Issue Date: 2017
Publisher: Elsevier
Source: Signal processing, 2017, v. 134, p. 275-284 How to cite?
Journal: Signal processing 
Abstract: Ultrasonography is widely used in clinical diagnosis and therapeutic procedures, but speckle noise often obscures important features and complicates interpretation and analysis of ultrasound images. In this regard, speckle reduction is a crucial prerequisite of many computer aided ultrasound diagnosis and treatment systems However, removing speckle noise while simultaneously preserving features in ultrasound images is a challenging task. We propose a novel optimization framework for speckle reduction by leveraging the concept of phase congruency and incorporating a feature asymmetry metric into the regularization term of the objective function to effectively distinguish the features and speckle noise. The feature asymmetry metric can productively separate features from speckle noise by analyzing the local frequency information. Compared with traditional methods employing intensity gradients as regularization terms, our framework is invariant to the intensity amplitude of features so that low contrast features are almost equally protected as high contrast features. In addition, rather than adopting the gradient descent, we propose a novel solver by decomposing the original non-convex optimization into solving several linear systems, leading to an efficient solution of the optimization. Owing to different penalties on speckle noise and features, our method can efficiently remove speckle noise and preserve features at the same time. Experiments on simulated and real ultrasound images demonstrate our method can better maintain features with speckle removal than state-of-the-art methods, especially for the low contrast features.
ISSN: 0165-1684
EISSN: 1872-7557
DOI: 10.1016/j.sigpro.2016.12.011
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