Hybrid deformable models for medical segmentation and registration

Dimitris N. Metaxas, Zhen Qian, Xiaolei Huang, Rui Huang, Ting Chen, Leon Axel

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

13 Scopus citations

Abstract

Deformable models have had great successes over the past 20 years in medical applications. We have recently developed new classes of deformable models which we term hybrid deformable models to automate the model initialization process and make improvements in segmentation and registration. In this paper we present several hybrid deformable methods we have been developing for segmentation and registration. These methods include Metamorphs, a novel shape and texture integration deformable model framework and the integration of deformable models with graphical models and learning methods. We first present a framework for the robust segmentation and tracking of the heart from tagged MRI images and second applications involving brain tumor segmentation as well as brain and cardiac shape registration.

Original languageEnglish (US)
Title of host publication9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06
DOIs
StatePublished - 2006
Event9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 - Singapore, Singapore
Duration: Dec 5 2006Dec 8 2006

Publication series

Name9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

Other

Other9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06
Country/TerritorySingapore
CitySingapore
Period12/5/0612/8/06

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

Keywords

  • Hybrid deformable model
  • Registration
  • Segmentation

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