Factor analysis of high-dimensional heterogeneous data for structural characterization

Alexei Manso Correa Machado; James C. Gee; Mario F.M. Campos

doi:10.1117/12.431098

3 July 2001 Factor analysis of high-dimensional heterogeneous data for structural characterization

Alexei Manso Correa Machado, James C. Gee, Mario F.M. Campos

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431098
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

In this work, we present a method for exploring the relationship among morphometric variables and the possible anatomic significance of these relationships. The analysis is based on the Jacobian determinant field resulting from the registration of a template to a set of subjects, which is represented as a factorial analytic model. In addition to morphometric variables, information about medical diagnosis is considered in the analytic model and corroborates to exploratory investigation of the relationship between regions of interest and pathologies. The definition of the number of factors to be considered is based on a robust analysis of the statistical fit of the factor model, instead of using as hoc criteria. The advantages of the proposed methodology are demonstrated in a study of shape differences between the corpora callosa of schizophrenic patients and normal controls. We show that the regions where these differences can occur can be determined by unsupervised analysis, indicating the method's potential for exploratory studies.

Citation Download Citation

Alexei Manso Correa Machado, James C. Gee, and Mario F.M. Campos "Factor analysis of high-dimensional heterogeneous data for structural characterization", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431098

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