Mixture modelling of medical magnetic resonance data
Ron Wehrens, Arjan W. Simonetti, L.M.C. Buydens · Journal of Chemometrics · 2002
Abstract 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.