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In this way, we obtained a T1 MRI template representative of the average neuroanatomy of all 36 participants in our study that is closer in terms of shape, size and internal configuration than any other existing template.

For each frame of a template representative, instead of selecting only one GMM and excluding the rest of the GMMs for a cluster M i as in MLTS, here we merge the original GMMs in each cluster M i into a new GMM and use the l new GMMs from the l clusters M i, i = 1,…, l to relabel the frame.

Figure 9 Phone accuracies (percent) versus the percentage of template representatives for MDTS (LLR) and MLTS (KL).

The remaining Gaussian components are used to construct a new GMM, and the new GMM is used as one of the l GMMs to label the corresponding frame of the template representative.

This indicates that the new GMMs were more effective in labeling the template representative frames, and the exclusion of the outlier GMMs was helpful, too.

The number of GMM clusters l in MLTS was set to 5, corresponding to using five GMMs to label each frame of a template representative.

This difference may be explained by the fact that MDTS simply selects a cluster center as a template representative, but MLTS further refines the GMM indices of each template representative frame to maximize the likelihood of the aligned frames in the corresponding cluster.

In MLTS, however, the selected template representative frames were relabeled by GMMs to maximize the likelihood of the aligned template frames, and template compression went further by generating new GMMs from the baseline GMMs and used the new GMMs to relabel the representative frames.

From Figure 9, we conclude that MLTS is more robust to using a small percentage of template representatives, and the selection percentage of 20% is a reasonable compromise between accuracy performance and computation and storage cost.

Here the number of template representatives was kept to be 20% of total templates.

Here we discuss how the three methods of (1) MDTS, (2) MLTS, and (3) template compression performed when using the LLR and KL distances and show the results in Figure 8, where the number of template representatives were kept to be 20% of the total templates for the three cases (further details are discussed in Section 5.3.5).