1,512 research outputs found
An adaptive speckle suppression filter for medical ultrasound imaging
Cataloged from PDF version of article.An adaptive smoothing technique for speckle suppression in medical B-scan ultrasonic imaging is presented. The technique is based on filtering with appropriately shaped and sized local kernels. For each image pixel, a filtering kernel, which fits to the local homogeneous region containing the processed pixel, is obtained through a local statistics based region growing technique. Performance of the proposed filter has been tested on the phantom and tissue images. The results show that the filter effectively reduces the speckle while preserving the resolvable details. The simulation results are presented in a comparative way with two existing speckle suppression methods. © 1995 IEE
Speckle Noise Reduction in Medical Ultrasound Images
Ultrasound imaging is an incontestable vital tool for diagnosis, it provides
in non-invasive manner the internal structure of the body to detect eventually
diseases or abnormalities tissues. Unfortunately, the presence of speckle noise
in these images affects edges and fine details which limit the contrast
resolution and make diagnostic more difficult. In this paper, we propose a
denoising approach which combines logarithmic transformation and a non linear
diffusion tensor. Since speckle noise is multiplicative and nonwhite process,
the logarithmic transformation is a reasonable choice to convert
signaldependent or pure multiplicative noise to an additive one. The key idea
from using diffusion tensor is to adapt the flow diffusion towards the local
orientation by applying anisotropic diffusion along the coherent structure
direction of interesting features in the image. To illustrate the effective
performance of our algorithm, we present some experimental results on
synthetically and real echographic images
Speckle Suppression in Ultrasonic Images Based on Undecimated Wavelets
An original method to denoise ultrasonic images affected by speckle is presented. Speckle is modeled as a signal-dependent noise corrupting the image. Noise reduction is approached as a Wiener-like filtering performed in a shift-invariant wavelet domain by means of an adaptive rescaling of the coefficients of an undecimated octave decomposition. The scaling factor of each coefficient is calculated from local statistics of the degraded image, the parameters of the noise model, and the wavelet filters. Experimental results demonstrate that excellent background smoothing as well as preservation of edge sharpness and fine details can be obtained
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