期刊文献+

基于纹理分析的自适应超声影像衰减补偿 被引量:2

Adaptive Ultrasound Image Attenuation Compensation Based on Texture Analysis
下载PDF
导出
摘要 针对人体组织衰减超声波所引起的超声图像整体亮度不均匀、组织结构模糊、难以辨别问题,首次提出一种基于图像纹理分析的自适应超声衰减能量补偿技术。首先描述超声波在软组织传播时能量的衰减模型,并引入组织的声衰减系数以及深度方向衰减能量的补偿量等概念。在介绍2维共生矩阵以及1维和差直方图表示的图像纹理参数后,阐述了准确估计组织声衰减系数及合理补偿超声衰减能量的基本原理。最后,通过已知声衰减系数的仿组织超声体模和人体组织超声影像做试验验证。实验结果不仅估计出准确的组织声衰减系数,亦可得到灰度值一致的超声影像。 In order to get uniform ultrasound images with clear structure,a novel and adaptive ultrasound attenuation compensation method based on image texture analysis was investigated.The attenuation process of acoustic signals transmitted in soft tissue was modeled,with the introduction of tissue attenuation coefficient and depth dependent attenuation compensation.After giving the 2-D co-occurrence matrices and 1-D sum and difference histograms based image texture parameters,the basic idea to precisely estimate tissue attenuation coefficient and properly compensate the attenuated ultrasound power was proposed.Algorithms were verified both in tissue-mimicking phantom with known attenuation coefficient and in vivo ultrasound images acquired from a clinical ultrasound system.Testing results presented accurate tissue attenuation coefficients and also uniform attenuation compensated ultrasound images.
出处 《四川大学学报(工程科学版)》 EI CAS CSCD 北大核心 2011年第3期139-144,共6页 Journal of Sichuan University (Engineering Science Edition)
关键词 超声成像 衰减 增益控制 图像分析 纹理 ultrasonic imaging attenuation gain control image analysis texture
  • 相关文献

参考文献10

  • 1Li Xiaoying, Liu Dong C. Estimation of ultrasound attenuation and its application to tissue heterogeneity study using nonlinear least square data fitting [ C ]. IEEE International Conference on Bioinformatics and Biomedical Engineering, 2009.
  • 2Kim H, Zagzebski J A, Varghese T. Estimation of ultrasound attenuation from broadband echo-signals using bandpass filtering [ J ]. IEEE Trans Ultrason, Ferroelect, and Freq Contr, 2008,55(5) :1153 - 1159.
  • 3Li Xiaoying, Liu Dong C. Estimation of local attenuation and its application to rationalized gain control[ C ]. International Conference on Bioinformatics and Biomedical Engineering, 2007 : 1267 - 1270.
  • 4Peters K J, Waag R C, Dalecki D, et al. Estimation of local attenuation from multiple views using compensated video signals [ J ]. Acustica, 1993,79 (3) :251 - 258.
  • 5Wear K A. A gaussian framework for modeling effects of frequency-dependent attenuation, frequency-dependent scatter- ing, and gating [ J ]. IEEE Trans Uhrason, Ferroelect, and Freq Contr,2002,49 ( 11 ) : 1572 - 1582.
  • 6Girault J M, Ossant F, Ouahabi A, et al. Time-varying autoregressive spectral estimation for ultrasound attenuatior/in tissue characterization[ J]. IEEE Trans Ultrason, Ferroelect, and Freq Contr, 1998,45 (3) :650 - 659.
  • 7Baldeweck T, Laugier P, Herment A, et al. Ultrasound attenuation estimation: interest in highly attenuating medium [ J ]. IEEE Trans Ultrason, Ferroelect, and Freq Contr, 1995,42 (1) :99-110.
  • 8Chen Q, Zagzebski J A. Simulation study of effects of speed of sound and attenuation on ultrasound lateral resoulation [ J ]. Ultrasound in Med Biol, 2004,30 ( 10) : 1297 - 1306.
  • 9Haralick R M, Shanmugan K, Dinstein I. Texture features for image classification [ J ]. IEEE Trans Syst Man, Cybern, 1973, SMC-3 (6) :610 - 621.
  • 10Unser M. Sum and difference histograms for texture classification[ J]. IEEE Trans Pattern Anal and Mach Intell, 1986, PAMI-8 ( 1 ) :118 - 125.

同被引文献23

  • 1冯波,翁杰,黄楠,屈树新,李孝红.结合学科特点和自身优势建立生物医学工程本科专业实验教学体系[J].实验技术与管理,2006,23(10):15-17. 被引量:14
  • 2Kuc R,Schwartz M. Estimating the acoustic attenuation coefficient slope for liver from reflected ultrasound signals[J].{H}IEEE TRANSACTIONS ON SONICS AND ULTRASONICS,1979,(5):353-361.
  • 3Yu Y J,Wang J. Backscatter-contour-attenuation joint estimation model for attenuation compensation in ultrasound imagery[J].{H}IEEE Transactions on Image Processing,2010,(10):2725-2736.
  • 4Haralick R M,Shanmugan K,Dinstein I. Texture features for image classification[J].IEEE Transactions System Man and Cyberntics,1973,(6):610-621.
  • 5He K M,Sun J,Tang X O. Guided image filtering[J].{H}IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,(6):1397-1409.
  • 6Lin C H,Sun Y N,Lin C J. A motion compounding technique for speckle reduction in ultrasound images[J].{H}JOURNAL OF DIGITAL IMAGING,2010,(3):246-257.
  • 7Shao D G,Liu P,Liu D C. Characteristic matching-based adaptive fast bilateral filter for ultrasound speckle reduction[J].{H}Pattern Recognition Letters,2013,(5):463-469.
  • 8Wear K A. A gaussian framework for modeling effects of frequency-dependent attenuation,frequency-dependent scattering,and gating[J].{H}IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control,2002,(11):1572-1582.
  • 9Treece G M,Gee A H,Prager R W. Ultrasound compounding with automatic attenuation compensation using paired angle scans[J].{H}Ultrasound in Medicine & Biology,2007,(4):630-642.
  • 10Knipp B S,Zagzebski J A,Wilson T A. Attenuation and backscatter estimation using video signal analysis applied to B-mode images[J].{H}ULTRASONIC IMAGING,1997,(3):221-233.

引证文献2

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部