Abstract
Within a manifold framework, the interpolation of tomographic image time series is investigated. To this end, the metamorphosis model of a manifold of images is taken into account. Based on a variational time discretization, discrete geodesic paths in this space of images are computed. The space discretization is based on finite elements spanned by tensor product cubic B-splines. An efficient implementation is obtained by utilizing graphics hardware and a proper combination of GPU and CPU computation. First results for time series of optical coherence tomography images of a macular degeneration demonstrate the applicability of this geometric concept.
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Berkels, B., Buchner, M., Effland, A., Rumpf, M., Schmitz-Valckenberg, S. (2017). GPU-Based Image Geodesics for Optical Coherence Tomography. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_21
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DOI: https://doi.org/10.1007/978-3-662-54345-0_21
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-54345-0
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