A novel denoising approach for Magnetic Resonance Images is presented within this manuscript. The method has been developed in the Bayesian statistical estimation theory framework and it is based on the Maximum A Posteriori approach. Markov Random Fields have been adopted for modeling the 3D image stack, making the proposed technique able to exploiting the spatial correlation between each pixel and its 3D neighborhood and tuning the filtering intensity according to it. First results on a simulated dataset confirm the effectiveness of the approach.

3D denosing of magnetic resonance images exploiting Bayesian estimation theory

Baselice, Fabio;Ferraioli, Giampaolo;Pascazio, Vito
2017-01-01

Abstract

A novel denoising approach for Magnetic Resonance Images is presented within this manuscript. The method has been developed in the Bayesian statistical estimation theory framework and it is based on the Maximum A Posteriori approach. Markov Random Fields have been adopted for modeling the 3D image stack, making the proposed technique able to exploiting the spatial correlation between each pixel and its 3D neighborhood and tuning the filtering intensity according to it. First results on a simulated dataset confirm the effectiveness of the approach.
9781509016426
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/65299
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