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Scale Invariant Metrics of Volumetric Datasets

Author(s)
Raskar, Ramesh; Raviv, Raviv
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Abstract
Nature reveals itself in similar structures of different scales. A child and an adult share similar organs yet dramatically differ in size. Comparing the two is a challenging task to a computerized approach as scale and shape are coupled. Recently, it was shown that a local measure based on the Gaussian curvature can be used to normalize the local metric of a surface and then to extract global features and distances. In this paper we consider higher dimensions; specifically, we construct a scale invariant metric for volumetric domains which can be used in analysis of medical datasets such as computed tomography (CT) and magnetic resonance imaging (MRI).
Date issued
2015-02
URI
http://hdl.handle.net/1721.1/97229
Department
Massachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Journal
SIAM Journal on Imaging Sciences
Publisher
Society for Industrial and Applied Mathematics
Citation
Raviv, Dan, and Ramesh Raskar. “Scale Invariant Metrics of Volumetric Datasets.” SIAM J. Imaging Sci. 8, no. 1 (January 2015): 403–425. © 2015, Society for Industrial and Applied Mathematics
Version: Final published version
ISSN
1936-4954

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