Ultrasound Speckle Reduction via \(L_{0}\) Minimization

  • Lei ZhuEmail author
  • Weiming Wang
  • Xiaomeng Li
  • Qiong Wang
  • Jing Qin
  • Kin-Hong Wong
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)


Speckle reduction is a crucial prerequisite of many computer-aided ultrasound diagnosis and treatment systems. However, most of existing speckle reduction filters concentrate the blurring near features and introduced 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 observed that the GAP, which integrates gradient and phase information, is extremely sparser in despeckled images than in speckled images. Based on this observation, we propose the \(L_{0}\) minimization framework to remove speckle noise and simultaneously preserve features in ultrasound images. It seeks for the \(L_{0}\) sparsity of the \(\textit{GAP}\) values, and such sparsity is achieved by reducing small \(\textit{GAP}\) values to zero in an iterative manner. Since features have larger \(\textit{GAP}\) magnitudes than speckle noise, the proposed \(L_{0}\) minimization is capable of effectively suppressing the speckle noise. Meanwhile, the rest of \(\textit{GAP}\) 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.


Ultrasound Image Spatial Filter Speckle Noise Speckled Image Speckle Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank reviewers for the various valuable comments. This work was supported by the Hong Kong Research Grants Council General Research Fund (Project No. CUHK 14202514), Hong Kong Innovation and Technology Fund for Hong Kong-Shenzhen Innovation Circle Funding Program (No. GHP/002/13SZ and SGLH20131010151755080), the Natural Science Foundation of Guangdong Province (Project No. 2014A030310381), the National Natural Science Foundation of China (Project No. 61233012 and 61305097), the Research and Development Project of Guangdong Key Laboratory of Robotics and Intelligent Systems (Grant No. ZDSYS20140509174140672), and Shenzhen Basic Research Program (Project No. JCYJ20150525092940988).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lei Zhu
    • 1
    Email author
  • Weiming Wang
    • 3
  • Xiaomeng Li
    • 1
  • Qiong Wang
    • 3
  • Jing Qin
    • 2
  • Kin-Hong Wong
    • 1
  • Pheng-Ann Heng
    • 1
    • 3
  1. 1.The Chinese University of Hong KongSha TinHong Kong
  2. 2.The Hong Kong Polytechnic UniversityKowloonHong Kong
  3. 3.Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality TechnologyShenzhen Institutes of Advanced Technology, Chinese Academy of ScienceShenzhenChina

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