A Set-based Hybrid Approach (SHA) for MRI segmentation

Jiang Liu; Tze Yun Leong; Kin Ban Chee; Boon Pin Tan; Borys Shuter; Shih Chang Wang

doi:10.1109/ICARCV.2006.345358

A Set-based Hybrid Approach (SHA) for MRI segmentation

Jiang Liu, Tze Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter.

Original language	English
Title of host publication	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06
DOIs	https://doi.org/10.1109/ICARCV.2006.345358
Publication status	Published - 2006
Externally published	Yes
Event	9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 - Singapore, Singapore Duration: 5 Dec 2006 → 8 Dec 2006

Publication series

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

Conference

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

ASJC Scopus subject areas

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

Access to Document

10.1109/ICARCV.2006.345358

Cite this

Liu, J., Leong, T. Y., Chee, K. B., Tan, B. P., Shuter, B., & Wang, S. C. (2006). A Set-based Hybrid Approach (SHA) for MRI segmentation. In 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 Article 4150342 (9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06). https://doi.org/10.1109/ICARCV.2006.345358

@inproceedings{36b73c00648942a4af6e238a5c2942df,

title = "A Set-based Hybrid Approach (SHA) for MRI segmentation",

abstract = "This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter.",

author = "Jiang Liu and Leong, {Tze Yun} and Chee, {Kin Ban} and Tan, {Boon Pin} and Borys Shuter and Wang, {Shih Chang}",

note = "Copyright: Copyright 2011 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.2006.345358",

language = "English",

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",

}

Liu, J, Leong, TY, Chee, KB, Tan, BP, Shuter, B & Wang, SC 2006, A Set-based Hybrid Approach (SHA) for MRI segmentation. in 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06., 4150342, 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, 5/12/06. https://doi.org/10.1109/ICARCV.2006.345358

A Set-based Hybrid Approach (SHA) for MRI segmentation. / Liu, Jiang; Leong, Tze Yun; Chee, Kin Ban et al.
9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06. 2006. 4150342 (9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Set-based Hybrid Approach (SHA) for MRI segmentation

AU - Liu, Jiang

AU - Leong, Tze Yun

AU - Chee, Kin Ban

AU - Tan, Boon Pin

AU - Shuter, Borys

AU - Wang, Shih Chang

PY - 2006

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N2 - This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter.

AB - This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter.

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DO - 10.1109/ICARCV.2006.345358

M3 - Conference contribution

AN - SCOPUS:34547154610

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 -

A Set-based Hybrid Approach (SHA) for MRI segmentation

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