A non parametric mixed-effect model for population analysis: Application to Alzheimer's disease data

Published on May 2, 2012 in ISBI (International Symposium on Biomedical Imaging)
· DOI :10.1109/ISBI.2012.6235757
Juan David Ospina8
Estimated H-index: 8
(French Institute of Health and Medical Research),
Oscar Acosta21
Estimated H-index: 21
(French Institute of Health and Medical Research)
+ 2 AuthorsPascal Haigron21
Estimated H-index: 21
(French Institute of Health and Medical Research)
In population analysis, the images of different groups can be compared to locate the effects of a particular disease or treatment and also to generate biomarkers that help in the diagnosis process. Voxel-Based Morphometry (VBM) is a set of widely extended techniques to compare groups of images. VBM involves image normalization, image smoothing, statistical map generation and correction for hypothesis testing. In this paper, we propose the use of a nonparametric mixed-effect model to study Alzheimer's Disease (AD). The proposed method can handle covariates and through the integration of the smoothing and statistical map generation, individual specificities can be controlled. Moreover, it allows the reconstruction of the typical shapes for each group and it can be advantageously used as another VBM implementation.
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