一种具有边缘保持特性的超声图像小波域阈值去噪新方法

孙俊喜; 陈亚珠

doi:null

您当前的位置：

首页 >

文章列表页 >

一种具有边缘保持特性的超声图像小波域阈值去噪新方法

论文 | 更新时间：2020-08-12

- 一种具有边缘保持特性的超声图像小波域阈值去噪新方法
- Novel speckle reduction for medical ultrasound images based on edge preservation
- 光学精密工程 2002年10卷第5期页码：429-433
- 作者机构：
  
  上海交通大学, 生物医学仪器研究所上海,200030
- 作者简介：
- 基金信息：
  
  上海市科技发展基金资助项目(No.994419027)()
- DOI：
  中图分类号： TH166
- 收稿日期：2002-03-18，
  
  修回日期：2002-10-08，
  
  网络出版日期：2002-10-15，
  
  纸质出版日期：2002-10-15
- 稿件说明：
移动端阅览
孙俊喜, 陈亚珠. 一种具有边缘保持特性的超声图像小波域阈值去噪新方法[J]. 光学精密工程, 2002,(5): 429-433

SUN Jun-xi, CHEN Ya-zhu. Novel speckle reduction for medical ultrasound images based on edge preservation[J]. Editorial Office of Optics and Precision Engineering, 2002,(5): 429-433
孙俊喜, 陈亚珠. 一种具有边缘保持特性的超声图像小波域阈值去噪新方法[J]. 光学精密工程, 2002,(5): 429-433 DOI：

SUN Jun-xi, CHEN Ya-zhu. Novel speckle reduction for medical ultrasound images based on edge preservation[J]. Editorial Office of Optics and Precision Engineering, 2002,(5): 429-433 DOI：

摘要

超声图像去噪是医学图像处理的研究热点之一

基于小波域阈值去噪技术及阈值选取方法的分析

提出一种新的医学超声图像小波域阈值去噪方法.这种方法采用半-软阈值去噪技术和广义交叉确认函数寻找阈值

在有效去噪的同时较好地保留了图像边缘细节.首先

把对数超声图像小波分解;然后

基于广义交叉确认函数寻找最小均方误差意义上的近似最优阈值

对所有的高频段采用半-软阈值去噪; 最后

经小波反变换和指数变换获得去噪后的超声图像

文末对超声图像小波域阈值去噪方法作出定性比较

并对算法的去噪性能给出定量分析.仿真实验和实际测试结果表明此方法是有效的、可行的.

Abstract

A novel speckle reduction for medical ultrasound images is presented based on the study of wavelet domain threshold-denoising and threshold-choosing. First

the logarithmic transform of the original image is decomposed into the multiscale wavelet domain. Then

semi-soft thresholding is used to reduce speckle noise. Finally

the denoised image is achieved by the invert DWT and the exponient transform of the estimated wavelet coefficients. Current state-of-the-art soft and hard thresholding methods based on universal threshold have been applied in actual ultrasound medical images. Compared with our method

the achieved performance improvement is quantified. Performance of the proposed method has been tested on Ultrasound images. The results show the method effectively reduces the speckle while preserving the edges of the original image.

关键词

Keywords

references

Zong X L, Laine A F, Geiser E A. Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing, [J] . IEEE Trans. Med. Imag. 1998,17:532-540.

Zong X, Geiser E A, Laine A F, et al. Homomorphic wavelet shrinkage and feature emphasis for speckle reduction and enhancement of echocardiographic images [J] .SPIE, 1996, 2710:658-667.

Xiang S H,Zhang Y T. Maximization of the signal-to-noise ratio for two dimensional medical ultrasound transducer sensitivity improvement by denoising wavelets[A] .in Proc. Int. Conf. Biomedical Engineering[C] Hong Kong,1996.

Zong X L, Laine A F,Geiser E A. Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing[J] . IEEE Trans. Med. Imag. 1998, 17: 532-540.

Asernaut H, April G. Properties of speckle integrated with a finite aperture and logarithmically transform[J] . J.Opt. Soc. Am,1976, 66:1160-1163.

Coifman R R, Donoho D L. Translation-invariant de-noising[P] . In wavelests and statistics, Lecture Notes in Statistics 103, Eds. Berlin, Germany:Springer-verlag,1995.

Donoho D L, Ohnstne I J. Adapting to unknown smoothness via wavelet shrinkage[J] .Amer. Stat. Assoc. 1995.90:1200-1224.

Jansen M, Malfait M,Bultheel A. Generalized cross validation forwavelet thresholding[J] . Signal Processing, January 1997,56(1):33-44.

浏览量

1003

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于空间依存的无参考图像质量评价