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Multi-scale classification of disease using structural MRI and wavelet transform

Item Type:Article
Title:Multi-scale classification of disease using structural MRI and wavelet transform
Creators Name:Hackmack, K. and Paul, F. and Weygandt, M. and Allefeld, C. and Haynes, J.D.
Abstract:Recently, multivariate analysis algorithms have become a popular tool to diagnose neurological diseases based on neuroimaging data. Most studies, however, are biased for one specific scale, namely the scale given by the spatial resolution (i.e. dimension) of the data. In the present study, we propose to use the dual-tree complex wavelet transform to extract information on different spatial scales from structural MRI data and show its relevance for disease classification. Based on the magnitude representation of the complex wavelet coefficients calculated from the MR images, we identified a new class of features taking scale, directionality and potentially local information into account simultaneously. By using a linear support vector machine, these features were shown to discriminate significantly between spatially normalized MR images of 41 patients suffering from multiple sclerosis and 26 healthy controls. Interestingly, the decoding accuracies varied strongly among the different scales and it turned out that scales containing low frequency information were partly superior to scales containing high frequency information. Usually, this type of information is neglected since most decoding studies use only the original scale of the data. In conclusion, our proposed method has not only a high potential to assist in the diagnostic process of multiple sclerosis, but can be applied to other diseases or general decoding problems in structural or functional MRI.
Keywords:Multi-Scale Classification, Multiple Sclerosis, Structural MRI, Support Vector Machine, Wavelet Transform
Publisher:Academic Press
Page Range:48-58
Date:1 August 2012
Official Publication:https://doi.org/10.1016/j.neuroimage.2012.05.022
PubMed:View item in PubMed

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