Oblique Random Forests for 3-D Vessel Detection Using Steerable Filters and Orthogonal Subspace Filtering

  • Matthias Schneider
  • Sven Hirsch
  • Gábor Székely
  • Bruno Weber
  • Bjoern H. Menze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7766)


We propose a machine learning-based framework using oblique random forests for 3-D vessel segmentation. Two different kinds of features are compared. One is based on orthogonal subspace filtering where we learn 3-D eigenspace filters from local image patches that return task optimal feature responses. The other uses a specific set of steerable filters that show, qualitatively, similarities to the learned eigenspace filters, but also allow for explicit parametrization of scale and orientation that we formally generalize to the 3-D spatial context. In this way, steerable filters allow to efficiently compute oriented features along arbitrary directions in 3-D. The segmentation performance is evaluated on four 3-D imaging datasets of the murine visual cortex at a spatial resolution of 0.7μm. Our experiments show that the learning-based approach is able to significantly improve the segmentation compared to conventional Hessian-based methods. Features computed based on steerable filters prove to be superior to eigenfilter-based features for the considered datasets. We further demonstrate that random forests using oblique split directions outperform decision tree ensembles with univariate orthogonal splits.


vessel segmentation orthogonal subspace filtering steerable filters oblique random forest 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Matthias Schneider
    • 1
  • Sven Hirsch
    • 1
  • Gábor Székely
    • 1
  • Bruno Weber
    • 2
  • Bjoern H. Menze
    • 1
  1. 1.Computer Vision LaboratoryETH ZurichSwitzerland
  2. 2.Institute of Pharmacology and ToxicologyUniversity of ZurichZurichSwitzerland

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