Wen Tiexiang, Gu Jia, Li Ling, Qin Wenjian, Wang Lei, Xie Yaoqin
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China The Shenzhen Key Laboratory for Low-Cost Healthcare, Shenzhen, People's Republic of China
Ultrason Imaging. 2016 Jul;38(4):254-75. doi: 10.1177/0161734615600676. Epub 2015 Aug 27.
Ultrasound 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 boundary preservation for ultrasound images.
超声因其实时和便携成像优势，是最重要的医学成像模态之一。然而，超声图像中的斑点会降低对比度分辨率和重要细节。已经开发了许多斑点滤波方法，但它们存在一些局限性，难以在斑点减少和边缘保留之间达到平衡。本文提出了一种非局部全变分（NLTV）滤波器的改进方法，用于减少超声图像中的斑点。通过对数压缩超声图像的信号相关噪声分布对斑点进行建模。本研究引入统计皮尔逊距离而非欧几里得距离，通过贝叶斯框架计算图像块之间的相似度。并使用分裂布雷格曼快速算法求解改进的NLTV去斑泛函。在合成和临床超声图像上的实验结果以及与一些经典和最新算法的比较，用于证明其在减少超声图像斑点噪声和保留组织边界方面的改进。
