This article presents an exploratory factor analytic approach to morphometry in which a high-dimensional set of shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to knowledge discovery and morphometric investigations.
Methods: The information about regional shape is extracted by registering a reference image to a set of test images. Based on the displacement fields obtained form image registration, the amount of pointwise volume enlargement or reduction is computed and statistically analyzed with the purpose of extracting a reduced set of common factors.
Experiments: The effectiveness and robustness of the method is demonstrated in a study of gender-related differences of the human corpus callosum anatomy, based on a sample of 84 right-handed normal controls.
Results: The method is able to automatically partition the structure into regions of interest, in which the most relevant shape differences can be observed. The confidence of results is evaluated by analyzing the statistical fit of the model and compared to previous experimental works.