Speckle reduction in medical ultrasound images using an unbiased non-local means method. (July 2016) - Sudeep, P.V.; Palanisamy, P.; Rajan, Jeny; Baradaran, Hediyeh; Saba, Luca; Gupta, Ajay; Suri, Jasjit S.
Abstract : Highlights: In this paper, an unbiased NLM speckle filter based on Gamma statistics has been proposed. The three parameter Gamma distribution function is used to fit the real US image in the proposed method. The scale and shape parameters of the Gamma distribution are estimated using the maximum likelihood (ML) method. The bias due to noise is expressed in terms of the Gamma parameters and is removed from the NLM filtered output. The excellent functioning of the proposed filter is well validated by experiments using both synthetic and real US images. Abstract: Enhancement of ultrasound (US) images is required for proper visual inspection and further pre-processing since US images are generally corrupted with speckle. In this paper, a new approach based on non-local means (NLM) method is proposed to remove the speckle noise in the US images. Since the interpolated final Cartesian image produced from uncompressed ultrasound data contaminated with fully developed speckle can be represented by a Gamma distribution, a Gamma model is incorporated in the proposed denoising procedure. In addition, the scale and shape parameters of the Gamma distribution are estimated using the maximum likelihood (ML) method. Bias due to speckle noise is expressed using these parameters and is removed from the NLM filtered output. The experiments on phantom images and real 2D ultrasound datasets show that the proposed method outperforms other related well-accepted methods, both in terms of objective and subjective evaluations. The results demonstrate that the proposed method has a better performance in both speckle reduction and preservation of structural features.