Abstract: We propose a multi-weighted probabilistic atlas to obtain accurate, robust, and reliable segmentation. The local
similarity measure is used as the weight to compute the probabilistic atlas, and the distance field is used as the weight to
incorporate the locality information of the atlas; the self-similarity is used as the weight to incorporate the local information of
target image to refine the probabilistic atlas. Experimental results with brain MRI images showed that the proposed algorithm
outperforms the common brain image segmentation methods and achieved a median Dice coefficient of 87.1% on the left
hippocampus and 87.6% on the right.