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Region Based Contour Detection by Dynamic Programming

  • Xiaoyi Jiang
  • Daniel Tenbrinck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)

Abstract

Dynamic programming (DP) is a popular technique for contour detection, particularly in biomedical image analysis. Although gradient information is typically used in such methods, it is not always a reliable measure to work with and there is a strong need of non-gradient based methods. In this paper we present a general framework for region based contour detection by dynamic programming. It is based on a global energy function which is approximated by a radial ray-wise summation to enable dynamic programming. Its simple algorithmic structure allows to use arbitrarily complex region models and model testing functions, in particular by means of techniques from robust statistics. The proposed framework was tested on synthetic data and real microscopic images. A performance comparison with the standard gradient-based DP and a recent non-gradient DP-based contour detection algorithm clearly demonstrates the superiority of our approach.

Keywords

Dynamic Programming Polar Space Gradient Magnitude Cost Matrix Contour Detection 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaoyi Jiang
    • 1
    • 2
    • 3
  • Daniel Tenbrinck
    • 1
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterGermany
  2. 2.European Institute for Molecular ImagingUniversity of MünsterGermany
  3. 3.Cluster of Excellence EXC 1003Cells in Motion, CiMMünsterGermany

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