Volume 1, Issue 2
An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images

Yiming Gao, Jie Huang, Xu Li, Hairong Liu & Xiaoping Yang

CSIAM Trans. Appl. Math., 1 (2020), pp. 256-276.

Published online: 2020-07

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  • Abstract

A good statistical model of speckle formation is useful to design a good speckle reduction model for clinical ultrasound images. We propose a new general distribution to describe the distribution of speckle in clinical ultrasound images according to a log-compression algorithm of clinical ultrasound imaging. A new variational model is designed to remove the speckle noise with the proposed general distribution. The efficiency of the proposed model is confirmed by experiments on synthetic images and real ultrasound images. Compared with previous variational methods which assign a designated distribution, the proposed method is adaptive to remove different kinds of speckle noise by estimating parameters to find suitable distribution. The experiments show that the proposed method can adaptively remove different types of speckle noise.

  • Keywords

Clinical ultrasound images, general distribution, speckle, adaptive.

  • AMS Subject Headings

65K10, 68U10, 94A08

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CSIAM-AM-1-256, author = {Yiming Gao , and Jie Huang , and Xu Li , and Hairong Liu , and Xiaoping Yang , }, title = {An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images}, journal = {CSIAM Transactions on Applied Mathematics}, year = {2020}, volume = {1}, number = {2}, pages = {256--276}, abstract = {

A good statistical model of speckle formation is useful to design a good speckle reduction model for clinical ultrasound images. We propose a new general distribution to describe the distribution of speckle in clinical ultrasound images according to a log-compression algorithm of clinical ultrasound imaging. A new variational model is designed to remove the speckle noise with the proposed general distribution. The efficiency of the proposed model is confirmed by experiments on synthetic images and real ultrasound images. Compared with previous variational methods which assign a designated distribution, the proposed method is adaptive to remove different kinds of speckle noise by estimating parameters to find suitable distribution. The experiments show that the proposed method can adaptively remove different types of speckle noise.

}, issn = {2708-0579}, doi = {https://doi.org/10.4208/csiam-am.2020-0010}, url = {http://global-sci.org/intro/article_detail/csiam-am/17179.html} }
TY - JOUR T1 - An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images AU - Yiming Gao , AU - Jie Huang , AU - Xu Li , AU - Hairong Liu , AU - Xiaoping Yang , JO - CSIAM Transactions on Applied Mathematics VL - 2 SP - 256 EP - 276 PY - 2020 DA - 2020/07 SN - 1 DO - http://doi.org/10.4208/csiam-am.2020-0010 UR - https://global-sci.org/intro/article_detail/csiam-am/17179.html KW - Clinical ultrasound images, general distribution, speckle, adaptive. AB -

A good statistical model of speckle formation is useful to design a good speckle reduction model for clinical ultrasound images. We propose a new general distribution to describe the distribution of speckle in clinical ultrasound images according to a log-compression algorithm of clinical ultrasound imaging. A new variational model is designed to remove the speckle noise with the proposed general distribution. The efficiency of the proposed model is confirmed by experiments on synthetic images and real ultrasound images. Compared with previous variational methods which assign a designated distribution, the proposed method is adaptive to remove different kinds of speckle noise by estimating parameters to find suitable distribution. The experiments show that the proposed method can adaptively remove different types of speckle noise.

Yiming Gao, Jie Huang, Xu Li, Hairong Liu & Xiaoping Yang. (2020). An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images. CSIAM Transactions on Applied Mathematics. 1 (2). 256-276. doi:10.4208/csiam-am.2020-0010
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