Hybrid deformable models for medical segmentation and registration

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

doi:10.1109/ICARCV.2006.345077

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 proceeding › Conference contribution

15 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 language	English (US)
Title of host publication	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06
DOIs	https://doi.org/10.1109/ICARCV.2006.345077
State	Published - 2006
Event	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 - Singapore, Singapore Duration: Dec 5 2006 → Dec 8 2006

Publication series

Name	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

Other

Other	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06
Country/Territory	Singapore
City	Singapore
Period	12/5/06 → 12/8/06

All Science Journal Classification (ASJC) codes

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

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10.1109/ICARCV.2006.345077

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Metaxas, D. N., Qian, Z., Huang, X., Huang, R., Chen, T., & Axel, L. (2006). Hybrid deformable models for medical segmentation and registration. In 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 Article 4150419 (9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06). https://doi.org/10.1109/ICARCV.2006.345077

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title = "Hybrid deformable models for medical segmentation and registration",

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.",

author = "Metaxas, {Dimitris N.} and Zhen Qian and Xiaolei Huang and Rui Huang and Ting Chen and Leon Axel",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 ; Conference date: 05-12-2006 Through 08-12-2006",

year = "2006",

doi = "10.1109/ICARCV.2006.345077",

language = "English (US)",

isbn = "1424403421",

series = "9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06",

booktitle = "9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06",

}

Metaxas, DN, Qian, Z, Huang, X, Huang, R, Chen, T & Axel, L 2006, Hybrid deformable models for medical segmentation and registration. in 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06., 4150419, 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06, 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06, Singapore, Singapore, 12/5/06. https://doi.org/10.1109/ICARCV.2006.345077

Hybrid deformable models for medical segmentation and registration. / Metaxas, Dimitris N.; Qian, Zhen; Huang, Xiaolei et al.
9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06. 2006. 4150419 (9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Hybrid deformable models for medical segmentation and registration

AU - Metaxas, Dimitris N.

AU - Qian, Zhen

AU - Huang, Xiaolei

AU - Huang, Rui

AU - Chen, Ting

AU - Axel, Leon

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:34547149263

SN - 1424403421

SN - 9781424403424

T3 - 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

BT - 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

T2 - 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

Y2 - 5 December 2006 through 8 December 2006

ER -

Hybrid deformable models for medical segmentation and registration

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