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Multi-scale Vessel Boundary Detection

  • Hüseyin Tek
  • Alper Ayvacı
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

In this paper, we present a robust and accurate method for the segmentation of cross-sectional boundaries of vessels found in contrast-enhanced images. The proposed algorithm first detects the edges along 1D rays in multiple scales by using mean-shift analysis. Second, edges from different scales are accurately and efficiently combined by using the properties of mean-shift clustering. Third, boundaries of vessel cross-sections are obtained by using local and global perceptual edge grouping and elliptical shape verification. The proposed algorithm is stable to (i) the case where the vessel is surrounded by other vessels or other high contrast structures, (iii) contrast variations in vessel boundary, and (iii) variations in the vessel size and shape. The accuracy of the algorithm is shown on several examples.

Keywords

Compute Tomography Angiography Magnetic Resonance Angiography Curve Segment Edge Element Edge Grouping 
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 2005

Authors and Affiliations

  • Hüseyin Tek
    • 1
  • Alper Ayvacı
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Siemens Corporate ResearchPrincetonUSA

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