Region growing Euclidean distance transforms

  • Olivier Cuisenaire
Poster Session A: Color & Texture, Enhancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


By propagating a vector for each pixel, we show that nearly Euclidean distance maps can be produced quickly by a region growing algorithm using hierarchical queues. Properties of the propagation scheme are used to detect potentially erroneous pixels and correct them by using larger neighbourhoods, without significantly affecting the computation time. Thus, Euclidean distance maps are produced in a time comparable to its commonly used chamfer approximations.


Background Pixel Large Neighbourhood Distance Transform Large Relative Error Dynamic Memory Allocation 
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.


  1. 1.
    P.E. Danielsson, Euclidean Distance Mapping, CGIP 14, 1980, 227–248.Google Scholar
  2. 2.
    Borgerfors, Distance Transformations in Arbitrary Dimensions, CVGIP 27, 1984, 321–345Google Scholar
  3. 3.
    G. Borgerfors, Distance Transformations in Digital Images, CVGIP 34, 1986, 344–371Google Scholar
  4. 4.
    H. Embrechts and D. Roose, A parallel Euclidean Distance Transformation Algorithm, CVIU 63, 1996, 15–26Google Scholar
  5. 5.
    J. Mullikin, The vector distance transform in two and three dimensions, CVGIP 54(6), 1992, 526–535Google Scholar
  6. 6.
    O. Cuisenaire, J.Ph. Thiran, B.Macq, Ch. Michel, A. De Volder and F. Marques, Automatic Registration of 3D MR Images with a Computerised Brain Atlas, SPIE Medical Imaging 1996, SPIE vol. 1710, 438–449.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Olivier Cuisenaire
    • 1
  1. 1.Telecommunication and Remote Sensing LaboratoryUniversité Catholique de LouvainBelgium

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