Segmentation by watersheds: definition and parallel implementation

  • Jos B. T. M. Roerdink
  • Arnold Meijster
Conference paper
Part of the Advances in Computing Science book series (ACS)


In the field of grey scale mathematical morphology the watershed transform, originally proposed by Digabel and Lantuéjoul, is frequently used for image segmentation [1, 9, 11]. It can be classified as a region-based segmentation approach. The intuitive idea underlying this method is that of flooding a landscape or topographic relief with water. Basins will fill up with water starting at local minima, and at points where water coming from different basins would meet, dams are built. When the water level has reached the highest peak in the landscape, the process is stopped. The set of dams thus obtained partitions the landscape into regions or ‘catchment basins’ separated by dams. These dams are called watershed lines or simply watersheds. A sketch is given in Fig. 1.


Short Path Parallel Implementation Priority Queue Mathematical Morphology Level Component 
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., Meyer, F.: The morphological approach to segmentation: the watershed transformation. In: Dougherty E. R. (ed.): Mathematical Morphology in Image Processing. New York: Marcel Dekker 1993 (chapter 12, pp. 433–481 ).Google Scholar
  2. [2]
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959).MathSciNetMATHCrossRefGoogle Scholar
  3. [3]
    Meijster, A., Roerdink, J. B. T. M.: The implementation of a parallel watershed algorithm. In: van Vliet J.C. (ed.): Proc. Computing Science in the Netherlands, 27–28 November, Utrecht. Amsterdam: Stichting Mathematisch Centrum 1995 (pp. 134–142 ).Google Scholar
  4. [4]
    Meijster, A., Roerdink, J. B. T. M.: A proposal for the implementation of a parallel watershed algorithm. In: Hlavac V., Sara R. (eds.): Computer Analysis of Images and Patterns. New York Heidelberg Berlin: Springer-Verlag 1995 (Lecture Notes in Computer Science, vol. 970, pp. 790–795 ).Google Scholar
  5. [5]
    Meijster, A., Roerdink, J. B. T. M.: Computation of watersheds based on parallel graph algorithms. In: Maragos P., Shafer R. W., Butt M. A. (eds.): Mathematical Morphology and its Applications to Image and Signal Processing. Dordrecht: Kluwer Acad. Publ. 1996 (pp. 305–312 ).Google Scholar
  6. [6]
    Meyer, F.: Topographic distance and watershed lines. Signal Processing 38, 113–125 (1994).MATHCrossRefGoogle Scholar
  7. [7]
    Moga, A.N., Viero, T., Dobrin, B.P., Gabbouj, M.: Implementation of a distributed watershed algorithm. In: Serra J., Soille P. (eds.): Mathematical Morphology and its Applications to Image Processing. Dordrecht: Kluwer Acad. Publ. 1994 (pp. 281–288 ).CrossRefGoogle Scholar
  8. [8]
    Najman, L., Schmitt, M.: Watershed of a continuous function. Signal Processing 38, 99–112 (1994).CrossRefGoogle Scholar
  9. [9]
    Serra, J.: Image Analysis and Mathematical Morphology. New York: Academic Press 1982.MATHGoogle Scholar
  10. [10]
    Vincent, L.: Algorithmes Morphologiques a Base de Files d’Attente et de Lacets. Extension aux Graphes. PhD thesis. Fontainebleau: Ecole Nationale Supérieure des Mines de Paris 1990.Google Scholar
  11. [11]
    Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (6), 583–598 (1991).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Jos B. T. M. Roerdink
  • Arnold Meijster

There are no affiliations available

Personalised recommendations