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Mixture modelling of medical magnetic resonance data
Author(s) -
Wehrens Ron,
Simonetti Arjan W.,
Buydens Lutgarde M. C.
Publication year - 2002
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.721
Subject(s) - cluster analysis , multivariate statistics , artificial intelligence , computer science , pattern recognition (psychology) , spectral clustering , magnetic resonance imaging , mixture model , data mining , machine learning , radiology , medicine
In clinical decision making, (semi‐)automatic unsupervised classification of data for diagnostic purposes is becoming more and more important. This paper describes the application of mixture modelling, a clustering where multivariate Gaussians are used to describe clusters in the data, to in vivo nuclear magnetic resonance data of patients with brain tumours. Images as well as localized spectra are analysed. The method is able to automatically generate meaningful classifications. Moreover, the results of clustering both the image and spectral data are in close agreement. Copyright © 2002 John Wiley & Sons, Ltd.