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Image Processing & Communications

The Journal of University of Technology and Life Sciences in Bydgoszcz

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2300-8709
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A Competitive Study of Graph Reduction Methods for Min S-T Cut Image Segmentation

Tomasz Węgliński
  • Lodz University of Technology, Institute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland
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/ Anna Fabijańska
  • Lodz University of Technology, Institute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland
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/ Jarosław Goclawski
  • Lodz University of Technology, Institute of Applied Computer Science, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland
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Published Online: 2015-03-27 | DOI: https://doi.org/10.1515/ipc-2015-0014

Abstract

When applied to the segmentation of 3D medical images, graph-cut segmentation algorithms require an extreme amount of memory and time resources in order to represent the image graph and to perform the necessary processing on the graph. These requirements actually exclude the graph-cut based approaches from their practical application. Hence, there is a need to develop the dedicated graph size reduction methods. In this paper, several techniques for the graph size reduction are proposed. These apply the idea of superpixels. In particular, two methods for superpixel creation are introduced. The results of applying the proposed methods to the segmentation of CT datasets using min-cut/max-flow algorithm are presented, compared and discussed.

References

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About the article

Published Online: 2015-03-27

Published in Print: 2014-09-01


Citation Information: Image Processing & Communications, ISSN (Online) 2300-8709, DOI: https://doi.org/10.1515/ipc-2015-0014.

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© Image Processing & Communications. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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