1 / 18

Ultrasound Image Denoising by Spatially Varying Frequency Compounding

1. Dept. Elect. Eng. Technion – Israel Institute of Technology. Ultrasound Image Denoising by Spatially Varying Frequency Compounding. Yael Erez , Yoav Y. Schechner , and Dan Adam. Ultrasound Problems. 7. Transmitter Receiver. Speckle noise. Blurring. Radial axis. Attenuation.

radha
Download Presentation

Ultrasound Image Denoising by Spatially Varying Frequency Compounding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 1 Dept. Elect. Eng. Technion – Israel Institute of Technology Ultrasound Image Denoising by Spatially Varying Frequency Compounding Yael Erez , Yoav Y. Schechner , and Dan Adam

  2. Ultrasound Problems 7 Transmitter Receiver Speckle noise Blurring Radial axis Attenuation System noise Lateral axis Erez, Schechner & Adam, Proc. DAGM 2006

  3. Previous Work 70s Wiener filter Space invariant Not using noise statistics 80s Compounding (frequency & spatial) Weighted median filter (Mcdicken et al.) 86 Local frequency diversity (Forsberg et al.) Smoothing Not handling attenuation 89 Anisotropic diffusion (Perona and Malik) 90 95 Non-linear Gaussian filters (Aurich) Low signal Late 90s Harmonic imaging Wavelets (Insana et al, Loi et al.) 01,04

  4. 8 Image Formation probe Received signal Velocity of acoustic wave in tissue Erez, Schechner & Adam, Proc. DAGM 2006

  5. Image Formation 9 Sector Probe Radial axis Sweeping beam Lateral axis Erez, Schechner & Adam, Proc. DAGM 2006

  6. 10 Lateral PSF D D High freq. = better (?) Low acoustic freq High acoustic freq Erez, Schechner & Adam, Proc. DAGM 2006

  7. Attenuation 11 r a object distance Low freq. = better (?) probe Erez, Schechner & Adam, Proc. DAGM 2006

  8. 15 Speckle Noise Low acoustic freq High acoustic freq Erez, Schechner & Adam, Proc. DAGM 2006

  9. Wave phenomenon 16 Wave interference Object blur: as if no interference Object Speckle Noise Erez, Schechner & Adam, Proc. DAGM 2006

  10. 17 PSF D D Depends on: • Radial distance • Acoustic frequency Low acoustic freq High acoustic freq Erez, Schechner & Adam, Proc. DAGM 2006

  11. 18 r = 7cm r = 11cm 1 White noise r = 15cm 0.8 0.6 0.4 0.2 0 -2 -1 0 1 2 Radial lag (mm) Measuring Noise Statistics Erez, Schechner & Adam , Proc. DAGM 2006

  12. Standard Pre-Processing 19 Time gain compensation Envelope detection Dynamic range compression RF line Sampling

  13. Speckle Noise 20 = log operation Iinear noise Erez, Schechner & Adam, Proc. DAGM 2006

  14. Model 21 … … … correlated noise !!! Erez, Schechner & Adam, Proc. DAGM 2006

  15. 22 y = Hx + n … … … Stochastic Reconstruction Erez, Schechner & Adam, Proc. DAGM 2006

  16. Best Linear Unbiased Estimator 23 Considering noise statistics Erez, Schechner & Adam, Proc. DAGM 2006

  17. Input: Dual Acoustic Frequency 24 Low acoustic freq High acoustic freq 5 6 7 Radial distance [cm] 8 9 10 11 Erez, Schechner & Adam, Proc. DAGM 2006

  18. Stochastic Freq. Compounding 25 Arithmetic mean Stochastic reconstruction 5 6 7 Radial distance [cm] 8 9 10 11 Erez, Schechner & Adam, Proc. DAGM 2006

More Related