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Conventional Non-Local Means (NLM) as one of the most powerful denoising filters especially for reduction of additive Gaussian noise is not successful in the case of Ultrasound (US) Images noise suppression. In the presence of additive Gaussian noise model, the NLM filter uses Euclidean distance similarity criterion to find similar patches and therefore it is not appropriate for US images which have noise with multiplicative and signal dependant nature. The more successful version of NLM filter for US images which is known as Optimized Bayesian NLM (OBNLM) is developed based on Pearson Distance similarity criterion to measure and find the similar patches. In this paper, we tried to improve the performance of NLM filter using appropriate fuzzy similarity criteria. The proposed filters are evaluated in objective and subjective manners with both synthetic phantom and real clinical US images. It is shown that the proposed methods have better ability for noise reduction comparing with the other state-of-art de-speckling filters.