Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79866
Title: Feature-preserving ultrasound speckle reduction via L-0 minimization
Authors: Zhu, L 
Wang, WM
Li, XM
Wang, Q
Qin, J 
Wong, KH
Choi, KS 
Fu, CW
Heng, PA
Keywords: Ultrasound speckle reduction
L-0 minimization
GAP
Half-quadratic splitting method Iteratively
Re-weighted least squares framework
Issue Date: 2018
Publisher: Elsevier
Source: Neurocomputing, 24 June 2018, v. 294, p. 48-60 How to cite?
Journal: Neurocomputing 
Abstract: Speckle reduction is a crucial prerequisite of many computer-aided ultrasound diagnosis and treatment systems. However, most existing speckle reduction filters tend to concentrate the blurring near the features and introduce the hole artifacts, making the subsequent processing procedures complicated. Optimization-based methods can globally distribute such blurring, leading to better feature preservation. Motivated by this, we propose a novel optimization framework based on L-0 minimization for feature preserving ultrasound speckle reduction. We present an observation that the GAP, which integrates gradient and phase information, is extremely sparser in despeckled images ( output) than in speckled images ( input). Based on this observation, we propose an L-0 minimization framework to remove speckle noise and simultaneously preserve features in the ultrasound images. It seeks for the L-0 sparsity of the GAP values, and such sparsity is achieved by reducing small GAP values to zero in an iterative manner. Since features have larger GAP magnitudes than speckle noise, the proposed L-0 minimization is capable of effectively suppressing the speckle noise. Meanwhile, the rest of GAP values corresponding to prominent features are kept unchanged, leading to better preservation of those features. In addition, we propose an efficient and robust numerical scheme to transform the original intractable L-0 minimization into several sub-optimizations, from which we can quickly find their closed-form solutions. Experiments on synthetic and clinical ultrasound images demonstrate that our approach outperforms other state-of-the-art despeckling methods in terms of noise removal and feature preservation.
URI: http://hdl.handle.net/10397/79866
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2018.03.009
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