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A Framework for Semi-automatic Fiducial Localization in Volumetric Images

  • Dénes Ákos Nagy
  • Tamás Haidegger
  • Ziv Yaniv
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8678)

Abstract

Fiducial localization in volumetric images is a common task performed by image-guided navigation and augmented reality systems. These systems often rely on fiducials for image-space to physical-space registration, or as easily identifiable structures for registration validation purposes. Automated methods for fiducial localization in volumetric images are available. Unfortunately, these methods are not generalizable as they explicitly utilize strong a priori knowledge, such as fiducial intensity values in CT, or known spatial configurations as part of the algorithm. Thus, manual localization has remained the most general approach, readily applicable across fiducial types and imaging modalities. The main drawbacks of manual localization are the variability and accuracy errors associated with visual localization. We describe a semi-automatic fiducial localization approach that combines the strengths of the human operator and an underlying computational system. The operator identifies the rough location of the fiducial, and the computational system accurately localizes it via intensity based registration, using the mutual information similarity measure. This approach is generic, implicitly accommodating for all fiducial types and imaging modalities. The framework was evaluated using five fiducial types and three imaging modalities. We obtained a maximal localization accuracy error of 0.35 mm, with a maximal precision variability of 0.5 mm.

Keywords

Manual Localization Target Registration Error Volumetric Image Augmented Reality System Phantom Data 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Dénes Ákos Nagy
    • 1
  • Tamás Haidegger
    • 2
  • Ziv Yaniv
    • 1
  1. 1.The Sheikh Zayed Institute for Pediatric Surgical InnovationChildren’s National Health SystemWashingtonUSA
  2. 2.Antal Bejczy Center for Intelligent RoboticsObuda UniversityHungary

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