Images of the MRI signal intensity are normally constructed by taking the magnitude of the complex-valued data. This results in a biased estimate of the true signal intensity. We c...
M. Dylan Tisdall, M. Stella Atkins, R. A. Lockhart
Abstract. This paper proposes a new adaptive wavelet-based Magnetic Resonance images denoising algorithm. A Rician distribution for background-noise modelling is introduced and a M...
— The problem of parameter estimation from Rician distributed data (e.g., magnitude Magnetic Resonance images) is addressed. The properties of conventional estimation methods are...
Jan Sijbers, Arnold Jan den Dekker, Paul Scheunder...
Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely perfor...
Kilian M. Pohl, John W. Fisher III, Ron Kikinis, W...
Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most prob...