Superpixel/voxel medical image segmentation algorithm based on the regional interlinked value.

Medical image segmentation can effectively overcome human perception with strong personal limitations. The superpixel/voxel segmentation method has strong adaptability and computational efficiency. It can effectively separate the diseased tissue from normal cells or bone and muscle, which is widely used. This paper proposes a superpixel/voxel medical image segmentation algorithm based on regional interlinked value and block (region) merging, which can segment the two-dimensional bone image and three-dimensional brain image. By computing the regional interlinked value, the proposed method can overcome the problem of the initial setting block size in the traditional superpixel/voxel segmentation method. Next, the blocks with the same features are merged. To segment the superpixel/voxel medical image, the final distance with the intensity feature, the location feature, and the gradient feature is considered. Compared with most state-of-the-art algorithms, the proposed method has strong robustness and efficiency, which provides a solid foundation for further image segmentation. [ABSTRACT FROM AUTHOR]