Informatics and Applications
2014, Volume 8, Issue 3, pp 79-89MATHEMATICAL STATISTICS METHODS AS A TOOL OF TWO-PARAMETRIC MAGNETIC-RESONANCE IMAGE ANALYSIS
- T. V. Yakovleva
- N. S. Kulberg
Abstract
The paper considers the methods of the magnetic-resonance image analysis, based on the solution of the so-called two-parametric task. The elaborated methods provide joint calculation of both statistical parameters - the mathematical expectation of the random value being analyzed and its dispersion, i. e., simultaneous estimation of both the useful signal and the noise. The considered variants of the task solution employ the methods of mathematical statistics: the maximum likelihood method and variants of the method of moments. A significant advantage of the elaborated two-parametric approach consists in the fact that it provides an efficient solution of nonlinear tasks including the tasks of noise suppression in the systems of magnetic-resonance visualization. Estimation of the sought-for parameters is based upon measured samples' data only and is not limited by any a priori suppositions. The paper provides the comparative analysis of the considered methodology's variants and presents the results of the computer simulation providing the statistical characteristics of the estimated parameters' shift and scatter while solving the task by various methods. The presented methods of the Rician signal's two-parametric analysis can be used within new information technologies at the stage of the stochastic values' processing.[+] References (20)
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[+] About this article
Title
MATHEMATICAL STATISTICS METHODS AS A TOOL OF TWO-PARAMETRIC MAGNETIC-RESONANCE IMAGE ANALYSISJournal
Informatics and Applications2014, Volume 8, Issue 3, pp 79-89
Cover Date
2014-03-31DOI
10.14357/19922264140309Print ISSN
1992-2264Publisher
Institute of Informatics Problems, Russian Academy of SciencesAdditional Links
Key words
Rice distribution; maximum likelihood method; method of moments; two-parametric analysis; signal-to-noise ratioAuthors
T. V. Yakovleva
and N. S. Kulberg
Author Affiliations
Dorodnicyn Computing Center of the Russian Academy of Sciences, 40 Vavilov Str., Moscow 119333, Russian Federation