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Local Regression Based Statistical Model Fitting

  • Matthias Amberg
  • Marcel Lüthi
  • Thomas Vetter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

Fitting statistical models is a widely employed technique for the segmentation of medical images. While this approach gives impressive results for simple structures, shape models are often not flexible enough to accurately represent complex shapes. We present a fitting approach, which increases the model fitting accuracy without requiring a larger training data-set. Inspired by a local regression approach known from statistics, our method fits the full model to a neighborhood around each point of the domain. This increases the model’s flexibility considerably without the need to introduce an artificial segmentation of the structure. By adapting the size of the neighborhood from small to large, we can smoothly interpolate between localized fits, which accurately map the data but are more prone to noise, and global fits, which are less flexible but constrained to valid shapes only. We applied our method for the segmentation of teeth from 3D cone-beam ct-scans. Our experiments confirm that our method consistently increases the precision of the segmentation result compared to a standard global fitting approach.

Keywords

Segmentation Result Shape Model Target Shape Active Shape Model Hand Shape 
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 2010

Authors and Affiliations

  • Matthias Amberg
    • 1
  • Marcel Lüthi
    • 1
  • Thomas Vetter
    • 1
  1. 1.Computer Science DepartmentUniversity of BaselSwitzerland

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