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

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Abstract

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.

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Acknowledgements

This work is supported by the Natural Science Foundations of China under Grant 61801202. We are also grateful to Prof. Qile Zhang from Quzhou People's Hospital, who provides the hospital dataset.

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  1. Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China

    Lingling Fang, Xin Wang & Mengyi Wang

  2. Nanchang Institute of Technology, Nanchang City, Jiangxi Province, China

    Lingling Fang

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  1. Lingling Fang
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Correspondence to Lingling Fang.

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Fang, L., Wang, X. & Wang, M. Superpixel/voxel medical image segmentation algorithm based on the regional interlinked value. Pattern Anal Applic 24, 1685–1698 (2021). https://doi.org/10.1007/s10044-021-01021-8

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