Hybrid Segmentation of Anatomical Data

Celina Imielinska, Dimitris Metaxas, Jayaram Udupa, Yinpeng Jin, Ting Chen

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

22 Scopus citations

Abstract

We propose new hybrid methods for automated segmentation of radiological patient data and the Visible Human data. In this paper, we integrate boundary-based and region-based segmentation methods which amplifies the strength but reduces the weakness of both approaches. The novelty comes from combining a boundary-based method, the deformable model-based segmentation with region-based segmentation methods, the fuzzy connectedness and Voronoi Diagram-based segmentation, to develop hybrid methods that yield high precision, accuracy and efficiency. This work is a part of a NTM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight).

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2001 - 4th International Conference, Proceedings
EditorsWiro J. Niessen, Max A. Viergever
PublisherSpringer Verlag
Pages1048-1057
Number of pages10
ISBN (Print)3540426973, 9783540454687
DOIs
StatePublished - 2001
Externally publishedYes
Event4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001 - Utrecht, Netherlands
Duration: Oct 14 2001Oct 17 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2208
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001
CountryNetherlands
CityUtrecht
Period10/14/0110/17/01

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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