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Wavelet based denoising techniques for ultrasound images

Clinical research has shown a clear correlation between white matter disorders of the neonatal brain and neuro-motoric handicap at a later age. The visual interpretation of ultrasound images is a proven method to detect white matter damage at in early stage. A problem, common to all medical ultrasound images, is the presence of speckle noise, which not only complicates the visual interpretation of images, but also the quantitative measurements. In this article we compare two wavelet based filtering methods which are applied in order to remove speckle noise and preserve details as well. The first one is a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov random field model. The second one is a new filtering method, based on a recursive filtering of detail images obtained from a wavelet decomposition of the image by using spatial filtering which is based on threshold decomposition and simple spatial rules.