Fast feature-preserving speckle reduction for ultrasound images via phase congruency

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• 作者：Lei Zhu^a ; Weiming Wang^c ; Jing Qin^b ; ; Kin-Hong Wong^a ; Kup-Sze Choi^b ; Pheng-Ann Heng^a ; ^c
• 关键词：Speckle reduction ; Phase congruency ; Feature asymmetry ; Iteratively weighted least squares ; Ultrasound images
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：May 2017
• 年：2017
• 卷：134
• 期：Complete
• 页码：275-284
• 全文大小：1607 K
• 卷排序：134

文摘

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. We propose a novel solver by decomposing the original nonconvex optimization into solving several linear systems, leading to an efficient solution of the optimization. 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.