Campus Units
Statistics
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
10-2009
Journal or Book Title
IEEE Transactions on Medical Imaging
Volume
28
Issue
10
First Page
1615
Last Page
1622
DOI
10.1109/TMI.2009.2024415
Abstract
Estimating the noise parameter in magnitude magnetic resonance (MR) images is important in a wide range of applications. We propose an automatic noise estimation method that does not rely on a substantial proportion of voxels being from the background. Specifically, we model the magnitude of the observed signal as a mixture of Rice distributions with common noise parameter. The Expectation-Maximization (EM) algorithm is used to estimate the parameters, including the common noise parameter. The algorithm needs initializing values for which we provide some strategies that work well. The number of components in the mixture model also need to be estimated en route to noise estimation and we provide a novel approach to doing so. Our methodology performs very well on a range of simulation experiments and physical phantom data. Finally, the methodology is demonstrated on four clinical datasets.
Copyright Owner
IEEE
Copyright Date
2009
Language
en
File Format
application/pdf
Recommended Citation
Maitra, Ranjan and Faden, David, "Noise Estimation in Magnitude MR Datasets" (2009). Statistics Publications. 79.
https://lib.dr.iastate.edu/stat_las_pubs/79
Comments
This is a manuscript of an article from IEEE Transactions on Medical Imaging 28 (2009): 1615, doi: 10.1109/TMI.2009.2024415. Posted with permission. Copyright 2009 IEEE.