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An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images

An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images

Year:    2020

Author:    Yiming Gao, Jie Huang, Xu Li, Hairong Liu, Xiaoping Yang

CSIAM Transactions on Applied Mathematics, Vol. 1 (2020), Iss. 2 : pp. 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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/csiam-am.2020-0010

CSIAM Transactions on Applied Mathematics, Vol. 1 (2020), Iss. 2 : pp. 256–276

Published online:    2020-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    21

Keywords:    Clinical ultrasound images general distribution speckle adaptive.

Author Details

Yiming Gao

Jie Huang

Xu Li

Hairong Liu

Xiaoping Yang