A Novel Approach for Bayesian Image Denoising Using a SGLI Prior
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Ultrasound despeckling for contrast enhancement
IEEE Transactions on Image Processing
Supervised restoration of degraded medical images using multiple-point geostatistics
Computer Methods and Programs in Biomedicine
Towards a software tool for ultrasound guided robotic hip resurfacing surgery
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the control theory.