Robust parameter estimation of intensity distributions for brain magnetic resonance images.

Détails

ID Serval
serval:BIB_B31DC4D6ABB5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Robust parameter estimation of intensity distributions for brain magnetic resonance images.
Périodique
IEEE Transactions on Medical Imaging
Auteur(s)
Schroeter P., Vesin J.M., Langenberger T., Meuli R.
ISSN
0278-0062
Statut éditorial
Publié
Date de publication
1998
Peer-reviewed
Oui
Volume
17
Numéro
2
Pages
172-186
Langue
anglais
Résumé
This paper presents two new methods for robust parameter estimation of mixtures in the context of magnetic resonance (MR) data segmentation. The head is constituted of different types of tissue that can be modeled by a finite mixture of multivariate Gaussian distributions. Our goal is to estimate accurately the statistics of desired tissues in presence of other ones of lesser interest. These latter can be considered as outliers and can severely bias the estimates of the former. For this purpose, we introduce a first method, which is an extension of the expectation-maximization (EM) algorithm, that estimates parameters of Gaussian mixtures but incorporates an outlier rejection scheme which allows to compute the properties of the desired tissues in presence of atypical data. The second method is based on genetic algorithms and is well suited for estimating the parameters of mixtures of different kind of distributions. We use this property by adding a uniform distribution to the Gaussian mixture for modeling the outliers. The proposed genetic algorithm can efficiently estimate the parameters of this extended mixture for various initial settings. Also, by changing the minimization criterion, estimates of the parameters can be obtained by histogram fitting which considerably reduces the computational cost. Experiments on synthetic and real MR data show that accurate estimates of the gray and white matters parameters are computed.
Mots-clé
Adolescent, Adult, Aged, Algorithms, Artifacts, Bias (Epidemiology), Brain/anatomy & histology, Computer Simulation, Female, Humans, Image Enhancement/methods, Image Processing, Computer-Assisted/statistics & numerical data, Likelihood Functions, Magnetic Resonance Imaging/statistics & numerical data, Male, Middle Aged, Models, Statistical, Monte Carlo Method, Normal Distribution, Stochastic Processes
Pubmed
Web of science
Création de la notice
08/04/2008 15:48
Dernière modification de la notice
03/03/2018 20:39
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