D-Snake: Image registration by as-similar-as-possible template deformation

Zohar Levi, Craig Gotsman

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We describe a snake-type method for shape registration in 2D and 3D, by fitting a given polygonal template to an acquired image or volume data. The snake aspires to fit itself to the data in a shape which is locally As-Similar-As-Possible (ASAP) to the template. Our ASAP regulating force is based on the Moving Least Squares (MLS) similarity deformation. Combining this force with the traditional internal and external forces associated with a snake leads to a powerful and robust registration algorithm, capable of extracting precise shape information from image data.

Original languageEnglish (US)
Article number6216370
Pages (from-to)331-343
Number of pages13
JournalIEEE Transactions on Visualization and Computer Graphics
Volume19
Issue number2
DOIs
StatePublished - 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Keywords

  • MLS deformation
  • Snake
  • registration

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