A Continuous Skeletonization Method Based on Distance Transform

  • Ting-Qin Yan
  • Chang-Xiong Zhou
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)


A skeleton extracted by distance map is located at geometrical center, but it is discrete, on the other hand, we can get a continuous skeleton with morphological algorithm, but the skeleton is not located at the geometrical center of the object image. To get a continuous skeleton that is located at geometrical center of the object image, a continuous skeletonization method based on distance transform is proposed in this paper. At first, the distance function is calculated with respect to the object boundary, which is defined as a new indicator for the skeletonization. Then, a thinning algorithm with five deletion templates is given, which can be applied to get a continuous and centered skeleton indicated by distance map. The performance of the proposed algorithm is compared with existing algorithms, experimental results confirm the superiority of our proposed approach.


Skeletonization Distance map Morphology Thinning 


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  1. 1.
    Komala Lakshmi, J., Punithavalli, M.: A Survey on Skeletons in Digital Image Processing. In: Proceedings. 2009 International Conference on Digital Image Processing, ICDIP 2009, pp. 260–269 (2009)Google Scholar
  2. 2.
    Srijeyanthan, K., Thusyanthan, A., Joseph, C.N., et al.: Skeletonization in a Aeal-Time Gesture Recognition System. In: Proceedings of the 2010 5th International Conference on Information and Automation for Sustainability, ICIAfS 2010, pp. 213–218 (2010)Google Scholar
  3. 3.
    Liu, X.X., Dean, M.N., Summers, A.P., et al.: Composite Model of the Shark’s Skeleton in Bending: A Novel Architecture for Biomimetic Design of Functional Compression Bias. Materials Science and Engineering C 30(8), 1077–1084 (2010)CrossRefGoogle Scholar
  4. 4.
    Rakesh, G., Rajpreet, K.: Skeletonization Algorithm for Numeral Patterns. International Journal of Signal Processing, Image Processing and Pattern Recognition 1(1), 63–72 (2008)Google Scholar
  5. 5.
    Latecki, L.J., Li, Q.N., Bai, X., et al.: Skeletonization Using SSM of the Distance Transform. In: Proceedings - International Conference on Image Processing, ICIP 2007, vol. 5, pp. V349–V352 (2007)Google Scholar
  6. 6.
    Xie, F., Xu, G., Cheng, Y., et al.: Human Body and Posture Recognition System Based on an Improved Thinning Algorithm. IET Image Processing 5(5), 420–428 (2011)CrossRefGoogle Scholar
  7. 7.
    Xia, H., Tucker, P.G.: Finite Volume Distance Field Solution Applied to Medial Aaxis Transform. In: 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, vol. 1(82), pp. 114–134 (2010)Google Scholar
  8. 8.
    Naccache, N.J., Shinghal, R.: Investigation Into the Skeletonnization Approach of Hilditch. Source: Pattern Recognition 3(17), 279–284 (1984)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ting-Qin Yan
    • 1
    • 2
  • Chang-Xiong Zhou
    • 1
  1. 1.Depart of Electronic Information EngineeringSuzhou Vocational UniversitySuzhouChina
  2. 2.Suzhou Key Lab of Digital Design & Manufacturing TechnologySuzhouChina

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