Waterball - IterativeWatershed Algorithm with Reduced Oversegmentation

  • Michal Swiercz
  • Marcin Iwanowski
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)


In this paper we present a new approach to watershed algorithm for segmentation of digital grey-scale images. The approach is derived from rainfall-type watershed methods, but utilises a different method of path tracing and iterative gradient image preparation to reduce oversegmentation and yield better results in object extraction. Sample results are discussed, with emphasis on their global correctness and practical applications.


Image Segmentation Gradient Image Rolling Ball Catchment Basin Edge Enhancement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beucher, S.: The watershed transformation applied to image segmentation. Scanning Microscopy International 6, 299–314 (1991)Google Scholar
  2. 2.
    Bieniek, A., Moga, A.: An efficient watershed algorithm based on connected components. Pattern Recognition 33(6), 907–916 (2000)CrossRefGoogle Scholar
  3. 3.
    Haris, K., Efstratiadis, S., Maglaveras, N., Katsaggelos, A.: Hybrid Image Segmentation Using Watersheds and Fast Region Merging. IEEE Transactions on image processing 7(12) (1998)Google Scholar
  4. 4.
    Lin, Y.C., Tsai, Y.P., Hung, Y.P., Shih, Z.C.: Comparison Between Immersion-Based and Toboggan- Based Watershed Image Segmentation. IEEE Transactions on Image Processing 15(3), 632–640 (2006)CrossRefGoogle Scholar
  5. 5.
    Osma-Ruiz, V., Godino-Llorente, J.I., Saenz-Lechon, N., Gomez-Vilda, P.: An improved watershed algorithm based on efficient computation of shortest paths. Pattern Recognition 40(3), 1078–1090 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Rambabu, C., Chakrabarti, I.: An efficient immersion-based watershed transform method and its prototype architecture. Journal of Systems Architecture 53(4), 210–226 (2007)CrossRefGoogle Scholar
  7. 7.
    Soille, P.: Morphological Image Analysis - principles and applications. Springer, Telos (1999)zbMATHGoogle Scholar
  8. 8.
    Stoev, S.L.: RaFSi - A Fast Watershed Algorithm Based on Rainfalling Simulation. In: 8th International Conference on Computer Graphics, Visualization, and Interactive Digital Media (WSCG 2000), pp. 100–107 (2000)Google Scholar
  9. 9.
    Sun, H., Yang, J., Ren, M.: A fast watershed algorithm based on chain code and application in image segmentation. Pattern Recognition Letters 26(9), 1266–1274 (2005)CrossRefGoogle Scholar
  10. 10.
    Świercz, M., Iwanowski, M.: Fast, Parallel Watershed Algorithm Based on Path Tracing. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 317–324. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Vincent, L.: Morphological Algorithms. In: Dougherty, E. (ed.) Mathematical Morphology in Image Processing, ch. 8, pp. 255–288. Marcel-Dekker, New York (1992)Google Scholar
  12. 12.
    Vincent, L., Soille, P.: Watersheds in digital spaces - an efficient solution based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  13. 13.
    Wang, J., Lu, H., Eude, W., Liu, Q.: A Fast Region Merging Algorithm For Watershed Segmentation. In: 7th International Conference on Signal Processing (ICSP 2004), Beijing, China, vol. 1, pp. 781–784 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michal Swiercz
    • 1
  • Marcin Iwanowski
    • 1
  1. 1.Institute of Control and Industrial ElectronicsWarsaw University of TechnologyWarsawPoland

Personalised recommendations