Abstract: A novel medical automatic image segmentation strategy based on guided filtering and multi-atlas is proposed to
achieve accurate, smooth, robust, and reliable segmentation. This framework consists of 4 elements: the multi-atlas
registration, which uses the atlas prior information; the label fusion, in which the similarity measure of the registration is used
as the weight to fuse the warped label; the guided filtering, which uses the local information of the target image to correct the
registration errors; and the threshold approaches used to obtain the segment result. The experimental results showed part
among the 15 brain MRI images used to segment the hippocampus region, the proposed method achieved a median Dice
coefficient of 86% on the left hippocampus and 87.4% on the right hippocampus. Compared with the traditional label fusion
algorithm, the proposed algorithm outperforms the common brain image segmentation methods with a good efficiency and
accuracy.