Design and implementation of denoising filter for echocardiographic images based on wavelet method

One of the most important aspects of diagnostic echocardiography is the need to reduce speckle noise and to improve image quality. Here the authors prepose a simple and effective filter design for image denoising and contrast enhancement based on the multiscale wavelet denoising method. Wavelet threshold algorithms replace wavelet coefficients of small magnitude with zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. Next, the authors estimate the distribution of noise within the echocardiographic image. Then they apply a wavelet threshold algorithm. A common way of the estimating the speckle noise level in coherent imaging to calculate the mean-to-standard-deviation ratio of the pixel intensity, often termed the Equivalent Number of Looks (ENL) over a uniform image area. Unfortunately, the authors found this measure not very robust, mainly because of the difficulty in identification of a uniform area in a real image. For this reason, they only use here the S/MSE ratio which corresponds to the standard SNR in the case of additive noise. The authors have simulated some echocardiographic images by specialized hardware for real-time application: processing of a 512*512 images takes about 1 min. The authors' experiments show that the optimal threshold level depends on the spectral content of the image. High spectral content tends to over-estimate the noise standard deviation estimation performed at the finest level of the DWT. As a result, a lower threshold parameter is required to get the optimal S/MSE. The standard WCS theory predicts a threshold that depends on the number of signal samples only.