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The lesion detectability by using ultrasound images is reduced by a factor of eight due to the presence of noise hampering the perception and extraction of fine details. Speckle noise occurrence is often undesirable, since it affects the tasks of human interpretation and diagnosis. The speckle is a random multiplicative noise caused by signals with different phase relations received from many sub-scatterers. It is a random mottling of the image with bright and dark spots, which obscures fine details. Speckle filtering is thus a critical pre-processing step in medical ultrasound imagery, provided that the features of interest for diagnosis are not lost. A new speckle reduction method is proposed for medical ultrasound imaging. It is based on temporal and spatial image filtering by a combination of two methods, Wavelet and diffusion transform. The method has two steps: the first one uses Wavelet transform in order to enhance the image contrast. The second step is based on nonlinear model-anisotropic complex diffusion filtering, because the human visual perception mechanism has nonlinear characteristics. As real images, the thyroid ultrasound images obtained by using a 5 MHz linear transducer were investigated. The diffusion coefficient was evaluated from the local pixel values, and wavelet shrinkage denoising used soft thresholding. Both subjective and objective evaluations of the experimental results show better performance of the proposed method in improving the signal to noise ratio compared to the conventional filtering methods.