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Steerable Features for Statistical 3D Dendrite Detection

  • Germán González
  • François Aguet
  • François Fleuret
  • Michael Unser
  • Pascal Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

Most state-of-the-art algorithms for filament detection in 3-D image-stacks rely on computing the Hessian matrix around individual pixels and labeling these pixels according to its eigenvalues. This approach, while very effective for clean data in which linear structures are nearly cylindrical, loses its effectiveness in the presence of noisy data and irregular structures.

In this paper, we show that using steerable filters to create rotationally invariant features that include higher-order derivatives and training a classifier based on these features lets us handle such irregular structures. This can be done reliably and at acceptable computational cost and yields better results than state-of-the-art methods.

Keywords

Feature Vector Hessian Matrix Ground Truth Data Image Stack Reference Orientation 
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 2009

Authors and Affiliations

  • Germán González
    • 1
  • François Aguet
    • 2
  • François Fleuret
    • 1
    • 3
  • Michael Unser
    • 2
  • Pascal Fua
    • 1
  1. 1.Computer Vision LabEcole Polytechnique Fédérale de LausanneSwitzerland
  2. 2.Biomedical Imaging GroupEcole Polytechnique Fédérale de LausanneSwitzerland
  3. 3.Idiap Research InstituteMartignySwitzerland

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