Systems and Means of Informatics
2014, Volume 24, Issue 3, pp 92109
TWOPARAMETRIC ANALYSIS OF MAGNETICRESONANCE IMAGES BY THE MAXIMUM LIKELIHOOD TECHNIQUE IN COMPARISON WITH THE ONEPARAMETRIC APPROXIMATION
 T. V. Yakovleva
 N. S. Kulberg
Abstract
The paper considers a method of the twoparametric analysis of magneticresonance image's data which allows getting a joint estimation of both the useful signal and the noise within the image being analyzed on the basis of the maximum likelihood principle. This technique presents an essentially new approach to data processing in the conditions of the Rice distribution and can be efficiently used in information technologies for solving a wide range of tasks connected with the Rician signals' filtering. Solving the twoparametric task is based on measured samples' data only and is not connected with a priori suppositions concerning the noise value which inevitably limit the precision of the oneparametric method. By means of computer simulation, a comparative analysis of the traditional oneparametric method and the elaborated two parametric method is made for estimation of the useful component of the signal forming the magneticresonance image. The statistical data for the shift and the spread of the soughtfor signal and noise parameters are calculated. The advantages of the twoparametric technique of data analysis are shown.
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[+] About this article
Title
TWOPARAMETRIC ANALYSIS OF MAGNETICRESONANCE IMAGES BY THE MAXIMUM LIKELIHOOD TECHNIQUE IN COMPARISON WITH THE ONEPARAMETRIC APPROXIMATION
Journal
Systems and Means of Informatics
Volume 24, Issue 3, pp 92109
Cover Date
20131130
DOI
10.14357/08696527140307
Print ISSN
08696527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Rice distribution; likelihood function; maximum likelihood method; noise dispersion; signaltonoise ratio
Authors
T. V. Yakovleva and N. S. Kulberg
Author Affiliations
Dorodnicyn Computing Center, Russian Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation
