Level set motion assisted non-rigid 3D image registration

Deshan Yang, Joseph O. Deasy, Daniel A. Low, Issam El Naqa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Medical imaging applications of rigid and non-rigid elastic deformable image registration are undergoing wide scale development. Our approach determines image deformation maps through a hierarchical process, from global to local scales. Vemuri (2000) reported a registration method, based on levelset evolution theory, to morph an image along the motion gradient until it deforms to the reference image. We have applied this level set motion method as basis to iteratively compute the incremental motion fields and then we approximated the field using a higher-level affine and non-rigid motion model. In such a way, we combine sequentially the global affine motion, local affine motion and local non-rigid motion. Our method is fully automated, computationally efficient, and is able to detect large deformations if used together with multi-grid approaches, potentially yielding greater registration accuracy.

Original languageEnglish
Title of host publicationMedical Imaging 2007
Subtitle of host publicationImage Processing
EditionPART 1
DOIs
StatePublished - 2007
EventMedical Imaging 2007: Image Processing - San Diego, CA, United States
Duration: Feb 18 2007Feb 20 2007

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
NumberPART 1
Volume6512
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2007: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA
Period02/18/0702/20/07

Keywords

  • Deformable image registration
  • Level set
  • Non-rigid image registration
  • Optical flow

Fingerprint

Dive into the research topics of 'Level set motion assisted non-rigid 3D image registration'. Together they form a unique fingerprint.

Cite this