Octree grid topology-preserving geometric deformable model (OTGDM)

Ying Bai, Xiao Han, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Topology-preserving geometric deformable models (TGDMs) are used to segment objects that have a known topology. Their accuracy is inherently limited by the resolution of the underlying computational grid. Although this can be overcome by using fine-resolution grids, both the computational cost and the size of the resulting surface increase dramatically. In this article, we present a new octree grid topology-preserving deformable model (OTGDM). OTGDMs refine grid resolution locally, thus maintaining computational efficiency and keep the surface mesh size manageable. Topology preservation is achieved by adopting concepts from a digital topology framework on octree grids that we have proposed previously. Details of OTGDM implementation are discussed, including grid generation, model initialization, numerical schemes, and final surface model extraction. Experiments on both mathematical phantoms and real medical images are used to demonstrate the advantages of OTGDMs.

Original languageEnglish (US)
Title of host publicationAdvances in Imaging and Electron Physics
PublisherAcademic Press Inc.
Pages1-34
Number of pages34
ISBN (Print)9780128000915
DOIs
StatePublished - 2014

Publication series

NameAdvances in Imaging and Electron Physics
Volume181
ISSN (Print)1076-5670

Keywords

  • Digital topology
  • adaptive grid
  • deformable model
  • image segmentation
  • isosurface
  • octree

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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