Design of nonlinear filter for echocardiographic images on wavelet-based method

One of the most significant features of diagnostic echocardiographic images is to reduce speckle noise and make better image quality. In this paper we propose a simple and effective filter design for image denoising and contrast enhancement based on a multiscale wavelet denoising. Wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. We estimate the distribution of noise within the echocardiographic image, then apply it to the fitness wavelet threshold algorithm. A common way of estimating the speckle noise level in coherent imaging is 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, we found this measure not very robust mainly because, of the difficulty of identifying a uniform area in a real image. For this reason, we only use here the S/MSE ratio which corresponds to the standard SNR in the case of additive noise. We have simulated some echocardiographic images with specialized hardware for real-time application; processing of a 512*512 image takes about 1 min. Our 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