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A Static SMC Sampler on Shapes for the Automated Segmentation of Aortic Calcifications

  • Kersten Petersen
  • Mads Nielsen
  • Sami S. Brandt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance model. It is suited for challenging problems where there is high uncertainty about the correct solution due to a low signal-to-noise ratio, clutter, occlusions or an erroneous model. Our method suits for segmentation tasks where the number of objects is not known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image. The method was inspired by shape particle filtering from de Bruijne and Nielsen, but shows substantial improvements to it. The principal contributions of this paper are as follows: (i) We introduce statistically motivated importance weights that lead to better performance and facilitate the application to new problems. (ii) We adapt the static sequential Monte Carlo (SMC) algorithm to the problem of image segmentation, where the algorithm proves to sample efficiently from high-dimensional static spaces. (iii) We evaluate the static SMC sampler on shapes on a medical problem of high relevance: the automated quantification of aortic calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease and mortality. Our results suggest that the static SMC sampler on shapes is more generic, robust, and accurate than shape particle filtering, while being computationally equally costly.

Keywords

Shape Particle Shape Model Automate Segmentation Importance Weight Target Distribution 
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

  • Kersten Petersen
    • 1
  • Mads Nielsen
    • 1
    • 2
  • Sami S. Brandt
    • 2
  1. 1.Department of Computer ScienceUniversity of CopenhagenDenmark
  2. 2.Synarc Imaging TechnologiesDenmark

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