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In order to find the complex shape of the 3D uniform geometry in medical image segmentation and to improve the segmentation accuracy, a MRI image segmentation algorithm based on 3D geometry adaptive split-merge is proposed. Firstly, a simple parallel hexahedron 12 kinds of 3D region segmentation strategy is built, and volume segmentation technology is used to divide the whole volume into many large homogeneous 3D geometric regions. Then, more small homogeneous regions in the volume are defined, so as to have a greater probability of survival in the coming merge step. Finally, a multi-level region merging phase involves only complex ASM tree leaves, concerning gray and size of common boundary areas, merging small regions to adjacent areas. Compared with other MRI image segmentation algorithms, the proposed method is robust to noise in the segmentation process, which improves the performance and accuracy of segmentation. In addition, the proposed method does not require training data sets.