Abstract: Speckle noise affects all coherent imaging systems
including medical ultrasound. In medical images, noise suppression
is a particularly delicate and difficult task. A tradeoff between noise
reduction and the preservation of actual image features has to be made
in a way that enhances the diagnostically relevant image content.
Even though wavelets have been extensively used for denoising
speckle images, we have found that denoising using contourlets gives
much better performance in terms of SNR, PSNR, MSE, variance and
correlation coefficient. The objective of the paper is to determine the
number of levels of Laplacian pyramidal decomposition, the number
of directional decompositions to perform on each pyramidal level and
thresholding schemes which yields optimal despeckling of medical
ultrasound images, in particular. The proposed method consists of the
log transformed original ultrasound image being subjected to contourlet
transform, to obtain contourlet coefficients. The transformed
image is denoised by applying thresholding techniques on individual
band pass sub bands using a Bayes shrinkage rule. We quantify the
achieved performance improvement.