A multiple geometric deformable model framework for homeomorphic 3D medical image segmentation

Xian Fan, Pierre Louis Bazin, John Bogovic, Ying Bai, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a 3D segmentation framework for multiple objects or compartments embedded as level sets. Thanks to a compact representation of the level set functions of multiple objects, the framework guarantees no overlap and vacuum, and leads to a computationally efficient evolution scheme largely independent of the number of objects. Appropriate topology constraints ensure not only that the topology of each object remains the same, but that the relationship between objects is also maintained. The decomposition of objects makes the framework specifically attractive to the segmentation of related anatomical regions or the parcellation of an organ, where relationships must be maintained and different evolution forces are needed on different parts of the objects interface. Examples of 3D whole brain segmentation and thalamic parcellation demonstrate the potential of our method for such segmentation tasks.

Original languageEnglish (US)
Title of host publication2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
DOIs
StatePublished - Sep 22 2008
Event2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Other

Other2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops
CountryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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