中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Nonlocal Total Variation-based Speckle Filtering for Ultrasound Images
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Nonlocal Total Variation-based Speckle Filtering for Ultrasound Images
Tiexiang Wen; Jia Gu; Ling Li; Wenjian Qin; Lei Wang; Yaoqin Xie
2016
Source PublicationUltrasonic Imaging
Subtype期刊论文
AbstractUltrasound is one of the most important medical imaging modalities for its real-time and portable imaging advantages. However, the contrast resolution and important details are degraded by the speckle in ultrasound images. Many speckle filtering methods have been developed, but they are suffered from several limitations, difficult to reach a balance between speckle reduction and edge preservation. In this paper, an adaptation of the nonlocal total variation (NLTV) filter is proposed for speckle reduction in ultrasound images. The speckle is modeled via a signal-dependent noise distribution for the log-compressed ultrasound images. Instead of the Euclidian distance, the statistical Pearson distance is introduced in this study for the similarity calculation between image patches via the Bayesian framework. And the Split-Bregman fast algorithm is used to solve the adapted NLTV despeckling functional. Experimental results on synthetic and clinical ultrasound images and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both speckle noise reduction and tissue boundaries preservation for ultrasound images.
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Indexed BySCI
Language英语
Department微创中心
Document Type期刊论文
Identifierhttp://ir.siat.ac.cn/handle/172644/10417
Collection医工所
AffiliationUltrasonic Imaging
Recommended Citation
GB/T 7714
Tiexiang Wen,Jia Gu,Ling Li,et al. Nonlocal Total Variation-based Speckle Filtering for Ultrasound Images[J]. Ultrasonic Imaging,2016.
APA Tiexiang Wen,Jia Gu,Ling Li,Wenjian Qin,Lei Wang,&Yaoqin Xie.(2016).Nonlocal Total Variation-based Speckle Filtering for Ultrasound Images.Ultrasonic Imaging.
MLA Tiexiang Wen,et al."Nonlocal Total Variation-based Speckle Filtering for Ultrasound Images".Ultrasonic Imaging (2016).
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