Scale invariant metrics of volumetric datasets

Dan Raviv, Ramesh Raskar

Research output: Contribution to journalArticlepeer-review

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).

Original languageEnglish
Pages (from-to)403-425
Number of pages23
JournalSIAM Journal on Imaging Sciences
Volume8
Issue number1
DOIs
StatePublished - 12 Feb 2015
Externally publishedYes

Keywords

  • Differential geometry
  • Laplace-Beltrami
  • Scale invariant
  • Shape analysis

Fingerprint

Dive into the research topics of 'Scale invariant metrics of volumetric datasets'. Together they form a unique fingerprint.

Cite this