  The objective of this study is to develop a patch-based labeling method that
cooperates with a label fusion using non-rigid registrations. We present a
novel patch-based label fusion method, whose selected patches and their weights
are calculated from a combination of similarity measures between patches using
intensity-based distances and labeling-based distances, where a previous
labeling of the target image is inferred through a label fusion method using
non-rigid registrations. These combined similarity measures result in better
selection of the patches, and their weights are more robust, which improves the
segmentation results compared to other label fusion methods, including the
conventional patch-based labeling method. To evaluate the performance and the
robustness of the proposed label fusion method, we employ two available
databases of T1-weighted (T1W) magnetic resonance imaging (MRI) of human
brains. We compare our approach with other label fusion methods in the
automatic hippocampal segmentation from T1W-MRI.
  Our label fusion method yields mean Dice coefficients of 0.847 and 0.798 for
the two databases used with mean times of approximately 180 and 320 seconds,
respectively. The collaboration between the patch-based labeling method and the
label fusion using non-rigid registrations is given in the several levels: (a)
The pre-selection of the patches in the atlases are improved, (b) The weights
of our selected patches are also more robust, (c) our approach imposes
geometrical restrictions, such as shape priors, and (d) the work-flow is very
efficient. We show that the proposed approach is very competitive with respect
to recently reported methods.
