A speckle reduction filter using wavelet-based methods for medical imaging application

One of the most significant features for diagnostic echocardiographic images is to reduce speckle noise and improve image quality. We propose a simple and effective filter design for image denoising and contrast enhancement based on a multiscale wavelet method. Wavelet threshold algorithms replace small magnitude wavelet coefficients 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. After we estimate the distribution of noise within an echocardiographic image, we apply it to a 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, this measure is not very robust, mainly due to the difficulty of identifying a uniform area in a real image. For this reason, we only use the S/MSE ratio, which corresponds to the standard SNR in case of additive noise. We have simulated some echocardiographic images by specialized hardware for a real-time application; processing of 512/spl times/512 images takes about 1 min. Our experiments show that the optimal threshold level depends on the spectral content of the image. With high spectral content, the noise standard deviation estimation performed at the finest level of the DWT tends to be over-estimated. Hence a lower threshold parameter is required to get the optimal S/MSE. The standard WCS theory predicts a threshold that depends only on the number of signal samples.