Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

11-2013

Journal or Book Title

Sankhya B

Volume

75

Issue

2

First Page

293

Last Page

318

DOI

10.1007/s13571-012-0055-y

Abstract

Magnitude magnetic resonance (MR) images are noise-contaminated measurements of the true signal, and it is important to assess the noise in many applications. A recently introduced approach models the magnitude MR datum at each voxel in terms of a mixture of up to one Rayleigh and an a priori unspecified number of Rice components, all with a common noise parameter. The Expectation-Maximization (EM) algorithm was developed for parameter estimation, with the mixing component membership of each voxel as the missing observation. This paper revisits the EM algorithm by introducing more missing observations into the estimation problem such that the complete (observed and missing parts) dataset can be modeled in terms of a regular exponential family. Both the EM algorithm and variance estimation are then fairly straightforward without any need for potentially unstable numerical optimization methods. Compared to local neighborhood- and wavelet-based noise-parameter estimation methods, the new EM-based approach is seen to perform well not only in simulation experiments but also on physical phantom and clinical imaging data.

Comments

This is a manuscript of an article from Sankhya B 75 (2013): 293, doi: 10.1007/s13571-012-0055-y. The final publication is available at Springer via http://dx.doi.org/10.1007/s13571-012-0055-y.

Copyright Owner

Indian Statistical Institute

Language

en

File Format

application/pdf

Published Version

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