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Despeckle Filtering in Medical Ultrasound Imaging

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Despeckle Filtering in Medical Ultrasound Imaging

Hairong Shi (1) Xingxing Wu (2)

(1) Department of Medical Physics, University of Wisconsin-Madison

(2) Department of Electrical and Computer Engineering, University of Wisconsin-Madison

- The medical Ultrasound B-scan image is acquired by summation of the echo signals from locally correlated scatterers in beam range.
- Locally correlated multiplicative noises from small scatterers corrupt ultrasound image. These noises are commonly called “speckles”.
- In many cases the speckle noise degrades the fine details and edge definition, limits the contrast resolution, limits the detect ability of small, low contrast lesions in body. And it should be filtered out.

- For research purpose,Radio-frequency (RF) data are collected. To show B-mode image, RF data are first envelope detected, and then logarithm compressed.
- The multiplicative speckle is converted into additive noise after logarithm compression, the noise is spatially correlated, and has a Rayleigh amplitude PDF:

- For fully developed speckle magnitude, the mean to standard deviation-pointwise SNR=1.9 (5.58dB)

- In this project, we implement 4 filtering methods:
- (1) Wiener Filter;
- (2) Anisotropic Diffusion Filter;
- (3) Wavelet Filter;
- (4) Adaptive Filter;

We use the following test images to evaluate the performance of the filters.

(1) 4 simulated inclusion phantoms with different contrast. Center frequency 3MHz, band width 40%, no attenuation. Contrast 10dB, 5dB,

-5dB and -10dB.

(2) An in-vitro B-mode image for a plaque from human carotid artery. The plaque is embedded in gelatin. From Aloka SSD2000 Medical Ultrasound system.

- Since the input filter g=1 in frequency domain, the Wiener filter is:
- The power spectrum of the underlying image is modeled as:
- Where σs2 can be replaced by the mean variance of the noised image σx2. μx and μy are frequency coordinators, the range is [-π, π).

The Power Spectrum of speckle pattern Sww is averaged from 12 simulated speckle patterns with image size 128*128.

- The restored images by Wiener filter are excellent:
- Most speckles are removed;
- Inclusions are clearly seen. even for 5dB contrast cases
- The background is uniform as we simulated.
- The main reason is that the averaged power spectrum of the noise is very close to the noise power in the noised images, so we can restore images well.

10dB

5dB

-5dB

-10dB

Plaque Sample

- The power spectrum of simulated noise can be applied well onto the real B-mode images:
- (1) The speckles are also removed efficiently
- (2)The structure of the materials are restored.
- There are still some speckles in restored images, which means the simulated noise power spectrum is not perfectly matched with the real ones.
- The rest speckles can be removed by median filters.
- The image qualities can be improved by unsharp mask and histogram stretch.

- Anisotropic diffusion is an efficient nonlinear technique for simultaneously performing contrast enhancement and noise reduction. It smoothes homogeneous image regions and retains image edges.
- The main concept of Anisotropic diffusion is diffusion coefficient. Perona and Malik (1990) proposed 2 options:
- Or

- The anisotropic diffusion method can be iteratively applied to the output image:
- Parameter k~[20,100], step sizeλ<=0.25.

- The anisotropic diffusion filter can restore noised image well:
- Speckles are removed and inclusions show clearly.
- In Anisotropic diffusion method, we don’t need know the noise pattern or power spectrum, this is the advantage over Wiener filter.
- The anisotropic diffusion method needs more computation time than Wiener Filter method.
- Parameter selection, iteration loop selection all affect the final results.

10dB

5dB

-5dB

-10dB

Plaque Sample

- The anisotropic diffusion method gives better contrast while removing speckles effectively.
- In fact, because the parameters in anisotropic diffusion method are adjustable, we can control parameters and choose the best image.

Image profiles before and after Wiener filter, and anisotropic diffusion are plotted.

Image becomes smoother after filtering.

- The K distribution model is a model for speckle statistics of ultrasound echo speckle.
- The K distribution is a good model for the echo envelope signal statistics when the scatter number densities are low.
- The model can accurately predict variations in the statistics with varying scatterer number.

K distribution pdf

The K distribution as a function of a

The restored image Y can be calculated by

Where X is the original image, is the image averaged value

The restored image Y can be calculated by

Where X is the original image, is the image averaged value. is the compensation coefficient.

Original image for

5dB inclusion phantom

Image of 5dB inclusion

Phantom after filtering

- The filter can smooth image locally based on some local statistics.
- This filter is easy to implement and the statistics is easy to estimate.
- There is no need to find an optimal solution.

- The wavelet techniques are widely used in the image processing, such as the image compression, image denoising.
- The wavelet filter has good image processing performance.
- We use thresholding method to despeckle.

Image decomposition Equation:

Decomposed image

Original image for

Carotid Artery Plaque

Image of plaque

After filtering

Original image for

5dB inclusion phantom

Image of 5dB inclusion

Phantom after filtering

To evaluate the performance of 4 different filters, we we take the same small region with pixel size 64*64, and calculate the mean-standard deviation ratio, i.e. pixel-wised SNR.

- Wiener filter, Anisotropic diffusion filter and k distribution based adaptive filter improve the SNR.
- Wavelet filter doesn’t improve the SNR very much.

- The Wiener filter can improve the image qualities well and simulated power spectrum of speckle can be applied on many situations.
- The Anisotropic diffusion filter can also despeckle well as long as we choose reasonable parameters, and it doesn’t need extra information of noise pattern.
- The K-distribution based adaptive filter can improve the image quality, the method is easy to implement and the statistics is easy to estimate and characterize.
- The wavelet filter is not highly suitable for removing the speckle in ultrasound images.