Accelerating the 3D Random Walker Image Segmentation Algorithm by Image Graph Reduction and GPU Computing

  • Jarosław Gocławski
  • Tomasz Węgliński
  • Anna Fabijańska
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 313)


In this paper the problem of image segmentation using the random walker algorithm was considered. When applied to the segmentation of 3D images the method requires an extreme amount of memory and time resources in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system. Having in mind these limitations the optimization of the random walker approach is proposed. In particular, certain techniques for the graph size reduction and method parallelization are proposed. The results of applying the introduced improvements to the segmentation of 3D CT datasets are presented and discussed. The analysis of results shows that the modified method can be successfully applied to the segmentation of volumetric images and on a single PC provides results in a reasonable time.


Minimum Span Tree Image Graph Sparse Linear System Host Memory Random Walker Algorithm 
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.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jarosław Gocławski
    • 1
  • Tomasz Węgliński
    • 1
  • Anna Fabijańska
    • 1
  1. 1.Institute of Applied Computer ScienceLodz University of TechnologyLodzPoland

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