Automated Cephalometric Landmark Localization Using Sparse Shape and Appearance Models

  • Johannes Keustermans
  • Dirk Smeets
  • Dirk Vandermeulen
  • Paul Suetens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


In this paper an automated method is presented for the localization of cephalometric landmarks in craniofacial cone-beam computed tomography images. This method makes use of a statistical sparse appearance and shape model obtained from training data. The sparse appearance model captures local image intensity patterns around each landmark. The sparse shape model, on the other hand, is constructed by embedding the landmarks in a graph. The edges of this graph represent pairwise spatial dependencies between landmarks, hence leading to a sparse shape model. The edges connecting different landmarks are defined in an automated way based on the intrinsic topology present in the training data. A maximum a posteriori approach is employed to obtain an energy function. To minimize this energy function, the problem is discretized by considering a finite set of candidate locations for each landmark, leading to a labeling problem. Using a leave-one-out approach on the training data the overall accuracy of the method is assessed. The mean and median error values for all landmarks are equal to 2.41 \(\textrm{mm}\) and 1.49 \(\textrm{mm}\), respectively, demonstrating a clear improvement over previously published methods.


Appearance Model Kernel Principal Component Analysis Local Image Descriptor Cephalometric Landmark Multivariate Gaussian Model 
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 2011

Authors and Affiliations

  • Johannes Keustermans
    • 1
  • Dirk Smeets
    • 1
  • Dirk Vandermeulen
    • 1
  • Paul Suetens
    • 2
  1. 1.Center for Processing Speech and Images, Department of Electrical EngineeringKatholieke Universiteit LeuvenBelgium
  2. 2.IBBT-K.U.Leuven Future Health departmentLeuvenBelgium

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