Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling

BMC Medical Imaging

Research Article

Qinghua Huang¹  Zhewei Yao²  Chengke Ye²  Feng Yang²  Shujun Liang²  Tiexiang Wen³ 

[1] College of Information Engineering, Shenzhen University, 518060, Shenzhen, People’s Republic of China;School of Electronic and Information Engineering, South China University of Technology, 510641, Guangzhou, People’s Republic of China;Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, People’s Republic of China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, People’s Republic of China;

关键词: Speckle reduction;    Spatiogram;    Kullback-Leibler (KL) divergence;    Nonlocal total variation;    Ultrasound image;   

DOI  :  10.1186/s12880-017-0231-7

 received in 2017-06-13, accepted in 2017-11-14,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundUltrasound imaging is safer than other imaging modalities, because it is noninvasive and nonradiative. Speckle noise degrades the quality of ultrasound images and has negative effects on visual perception and diagnostic operations.MethodsIn this paper, a nonlocal total variation (NLTV) method for ultrasonic speckle reduction is proposed. A spatiogram similarity measurement is introduced for the similarity calculation between image patches. It is based on symmetric Kullback-Leibler (KL) divergence and signal-dependent speckle model for log-compressed ultrasound images. Each patch is regarded as a spatiogram, and the spatial distribution of each bin of the spatiogram is regarded as a weighted Gamma distribution. The similarity between the corresponding bins of the two spatiograms is computed by the symmetric KL divergence. The Split-Bregman fast algorithm is then used to solve the adapted NLTV object function. Kolmogorov-Smirnov (KS) test is performed on synthetic noisy images and real ultrasound images.ResultsWe validate our method on synthetic noisy images and clinical ultrasound images. Three measures are adopted for the quantitative evaluation of the despeckling performance: the signal-to-noise ratio (SNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). For synthetic noisy images, when the noise level increases, the proposed algorithm achieves slightly higher SNRS than that of the other two algorithms, and the SSIMS yielded by the proposed algorithm is obviously higher than that of the other two algorithms. For liver, IVUS and 3DUS images, the NIQE values are 8.25, 6.42 and 9.01, all of which are higher than that of the other two algorithms.ConclusionsThe results of the experiments over synthetic and real ultrasound images demonstrate that the proposed method outperforms current state-of-the-art despeckling methods with respect to speckle reduction and tissue texture preservation.

【 授权许可】

CC BY   
© The Author(s). 2017

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