Supervised classification for customized intraoperative augmented reality visualization

Olivier Pauly, Amin Katouzian, Abouzar Eslami, Pascal Fallavollita, Nassir Navab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

In this paper, we present a fusion algorithm supplemented with appropriate visualization by selecting relevant information from different modalities in mixed and augmented reality (AR). This encompasses a learning based method upon relevance of information, defined by an expert, which ultimately enables confident interventional decisions based on mixed reality (MR) images. The performance of our developed fusion and tailored visualization techniques was evaluated by employing X-ray/optical images during surgery and validated qualitatively using a 5-point Likert scale. Our observations indicated that the proposed technique provided semantic contextual information about underlying pixels and in general was preferred over the traditional pixel-wise linear alpha-blending method.

Original languageEnglish
Title of host publicationISMAR 2012 - 11th IEEE International Symposium on Mixed and Augmented Reality 2012, Science and Technology Papers
Pages311-312
Number of pages2
DOIs
StatePublished - 2012
Event11th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2012 - Atlanta, GA, United States
Duration: 5 Nov 20128 Nov 2012

Publication series

NameISMAR 2012 - 11th IEEE International Symposium on Mixed and Augmented Reality 2012, Science and Technology Papers

Conference

Conference11th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR 2012
Country/TerritoryUnited States
CityAtlanta, GA
Period5/11/128/11/12

Keywords

  • CamC
  • Fusion
  • Medical Augmented Reality
  • Relevant Information
  • Visualization
  • X-ray

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