3D AUTOCUT: a 3D segmentation algorithm based on cellular automata

3D AUTOCUT: a 3D segmentation algorithm based on cellular automata

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An improved 3D cellular automaton segmentation algorithm is proposed. The purpose is to create a tri-directional automaton in order to segment 3D volumes. The improvement is that a force is created in the RGB space in order to improve the result of the 3D algorithm. Experiments on several synthetic volumes have shown that the proposed method achieves better segmentation results and efficiency than the other methods.


    1. 1)
    2. 2)
      • 2. Nascimento, J.C., Carneiro, G.: ‘Deep learning on sparse manifolds for faster object segmentation’, Trans. Image Process., 2017, PP, (99), pp. 11.
    3. 3)
    4. 4)
      • 4. Chabrier, S., Rosenberger, C., Laurent, H., et al: ‘Evaluating the segmentation result of a gray level image’. 2004 12th European Signal Processing Conf., September 2004, pp. 953956.
    5. 5)
      • 5. Razali, M.R.M., Ahmad, N.S., Hassan, R., et al: ‘Sobel and canny edges segmentations for the dental age assessment’. 2014 Int. Conf. on Computer Assisted System in Health, December 2014, pp. 6266.
    6. 6)
      • 6. Farhangi, M.M., Frigui, H., Seow, A., et al: ‘3d active contour segmentation based on sparse linear combination of training shapes (scots)’, Trans. Med. Imaging, 2017, PP, (99), pp. 11.
    7. 7)
      • 7. Gao, M., Chen, H., Zheng, S., et al: ‘Texture image segmentation using fused features and active contour’. 2016 23rd Int. Conf. on Pattern Recognition (ICPR), December 2016, pp. 20362041.
    8. 8)
      • 8. Xu, C., Sun, Y., Lombaert, H., et al: ‘A multilevel banded graph cuts method for fast image segmentation’. IEEE Int. Conf. on Computer Vision, 2005, vol. 01, no. undefined, pp. 259265.
    9. 9)
      • 9. Srinark, T., Kambhamettu, C., Kambhamettu, R.: ‘An approach for 3d segmentation on multiresolution surfaces’. 2003.
    10. 10)
    11. 11)
      • 11. Boykov, Y., Jolly, M.-P.: ‘Interactive organ segmentation using graph cuts’ (Springer Berlin Heidelberg, Berlin, Heidelberg, 2000), pp. 276286.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 15. Gonzalez, R.C., Woods, R.E., Eddins, S.I.: ‘Digital image processing’ (Gatesmark Publishing, Knoxville, 2009, 3rd edn.).
    16. 16)

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