June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Triangle mesh segmentation and reconstruction of HD 3D OCT data sets for augmented reality simulation purposes
Author Affiliations & Notes
  • Carl Glittenberg
    Dept of Ophthalmology, Rudolph Foundation Hospital, Vienna, Austria
  • Susanne Binder
    Dept of Ophthalmology, Rudolph Foundation Hospital, Vienna, Austria
  • Footnotes
    Commercial Relationships Carl Glittenberg, None; Susanne Binder, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 5521. doi:
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      Carl Glittenberg, Susanne Binder; Triangle mesh segmentation and reconstruction of HD 3D OCT data sets for augmented reality simulation purposes. Invest. Ophthalmol. Vis. Sci. 2013;54(15):5521.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose: To develop and evaluate a method of creating 3D triangle meshes based on reflection intensity gradient ranges of 3D OCT data sets for the purpose of fluid dynamic simulation, surgical simulation, and augmented reality simulation.

Methods: Intensity range based particle clouds of different retinal structures were created using a Realflow™ bitmap emitter. A post processed image sequence of the previously gathered spectral domain OCT data was used as input to the emitter. The particle clouds were converted to polygon objects by using triangular meshing (standard particle mesh) with standard tension and relaxation filtering. The resulting geometry was automatically ported into Cinema 4d™ using the Nextlimit™ plugin. In Cinema 4D™ the geometry was automatically outfitted with textures corresponding to the relevant segmented retinal structures and visualized using CUDA based GPU rendering. Inside Cinema 4d the geometry was tested for interaction capability with the on board physics simulation algorithms. After polygon reduction, the textured geometry was ported to ARmedia™ iOS augmented reality player for augmented reality testing on a second generation iPad™.

Results: It was possible to create triangle meshes of sufficient quality to interact correctly with the physics simulation algorithms of Cinema 4D™. Additionally it was possible to export these meshes into a functioning augmented reality platform on the iPad™. The particle clouds contained between 500 thousand and 1 million particles. The resulting geometry contained up to 300 thousand faces per data set. Particle cloud creation and meshing took approximately 30-45 seconds depending on the complexity of the structures in the data.

Conclusions: This technology has the potential of changing the way in which we interact with and interpret OCT data. The speed of conversion and the triangle density still needs to be optimized

Keywords: 549 image processing • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • 551 imaging/image analysis: non-clinical  

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