Construct adaptive template array for magnetic resonance images

Dealing with large number of brain images in group analysis involves two kinds of analysis. One is to extract the information and relations in the population of brains, and the other is to combine the information of individual brain in the group. Linear or nonlinear dimension reduction algorithms are the main tools to perform the first analysis, which is to show the information of the population. The hidden relations in the distribution are therefore able to be visualized in low-dimensional and visible space. Image registration is the critical part of the second analysis, which is to integrate the information or statistics of individual brains. The statistics are registered to a template which is commonly the mean brain image of the population so that the statistics from different subjects can be compared in the same stereotaxic space. The process of registering images to the template is called normalization. The quality of registration decides the normalization and the interpretability of results. This work constructed ordered representations, from a set of brain images, as the multiple templates. The ordered representations are derived from self-organizing map. A novel method, transformation diversion, based on the ordered representations is proposed to improve the registration, which is a non-linear deformation, in a general manner. The discriminative low-dimensional representation of the population of Alzheimer disease and normal subjects are also shown. The set of ordered representations not only shows the population information but also improve the normalization process.