Discriminative analysis for image-based studies

P. Golland, B. Fischl, M. Spiridon, N. Kanwisher, R. L. Buckner, M. E. Shenton, R. Kikinis, A. M. Dale AGrimson WEL.
Proceedings of the 5th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI 2002
Pages 508-515
2002

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Abstract

In this paper, we present a methodology for performing statistical analysis for image-based studies of differences between populations and describe our experience applying the technique in several different population comparison experiments. Unlike traditional analysis tools, we consider all features simultaneously, thus accounting for potential correlations between the features. The result of the analysis is a classifier function that can be used for labeling new examples and a map over the original features indicating the degree to which each feature participates in estimating the label for any given example. Our experiments include shape analysis of subcortical structures in schizophrenia, cortical thinning in healthy aging and Alzheimer's disease and comparisons of fMRI activations in response to different visual stimuli.

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Reference

Golland P, Fischl B, Spiridon M, Kanwisher N, Buckner RL, Shenton ME, Kikinis R, WEL AMDA. Discriminative analysis for image-based studies. In T Dohi, R Kikinis, eds., Proceedings of the 5th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI 2002. Springer-Verlag GmbH, 2002;508-515.

Grants

NIH/NIMH 2K02 MH01110, NIH RO1 MH50747, NSF IIS 9610249, Martinos Center, NIH R01 RR11747, NIH P41 RR13218, NSF ERC 9731748

Research area

shapeanalysis