3D Model-based method for vessel segmentation in TOF-MRA

Kui Fang; De Feng Wang; L. M. Lui; Shou Jun Zhou; W. C.W. Chu; A. T. Ahuja; Pheng Ann Heng

doi:10.1109/ICMLC.2011.6016988

3D Model-based method for vessel segmentation in TOF-MRA

Kui Fang^*
, De Feng Wang
, L. M. Lui
, Shou Jun Zhou
, W. C.W. Chu
, A. T. Ahuja
, Pheng Ann Heng

^*Corresponding author for this work

Shenzhen Institute of Advanced Technology
Shenzhen Key Lab of Neuro Psychiatric Modulation
Chinese University of Hong Kong

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

6 Scopus citations

Abstract

In this paper, an automatic method to segment the blood vessel for 3D MRA (Magnetic Resonance Angiography) is presented. The segmentation process classifies MRA data into two parts: background and blood vessels. The process includes statistical model based on the voxel intensity and MRF model based on the context information of voxels. Both the models were built on 3D voxel, rather than on 2D. The proposed method is tested on the 3D Time-Of-Flight (TOF)-MRA data. The segmentation results give a good performance in extracting blood vessels.

Original language	English
Title of host publication	Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Publisher	IEEE Computer Society
Pages	1607-1611
Number of pages	5
ISBN (Print)	9781457703065
DOIs	https://doi.org/10.1109/ICMLC.2011.6016988
State	Published - 2011
Externally published	Yes
Event	10th International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 10 Jul 2011 → 13 Jul 2011

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	4
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	10th International Conference on Machine Learning and Cybernetics, ICMLC 2011
Country/Territory	China
City	Guilin, Guangxi
Period	10/07/11 → 13/07/11

Keywords

Context information
Markov random field
Statistical model
Vessel segmentation

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10.1109/ICMLC.2011.6016988

Cite this

Fang, K., Wang, D. F., Lui, L. M., Zhou, S. J., Chu, W. C. W., Ahuja, A. T., & Heng, P. A. (2011). 3D Model-based method for vessel segmentation in TOF-MRA. In Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 (pp. 1607-1611). Article 6016988 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 4). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2011.6016988

@inproceedings{7d82e6e5a35f49f7b35306cc3a1e2591,

title = "3D Model-based method for vessel segmentation in TOF-MRA",

abstract = "In this paper, an automatic method to segment the blood vessel for 3D MRA (Magnetic Resonance Angiography) is presented. The segmentation process classifies MRA data into two parts: background and blood vessels. The process includes statistical model based on the voxel intensity and MRF model based on the context information of voxels. Both the models were built on 3D voxel, rather than on 2D. The proposed method is tested on the 3D Time-Of-Flight (TOF)-MRA data. The segmentation results give a good performance in extracting blood vessels.",

keywords = "Context information, Markov random field, Statistical model, Vessel segmentation",

author = "Kui Fang and Wang, \{De Feng\} and Lui, \{L. M.\} and Zhou, \{Shou Jun\} and Chu, \{W. C.W.\} and Ahuja, \{A. T.\} and Heng, \{Pheng Ann\}",

year = "2011",

doi = "10.1109/ICMLC.2011.6016988",

language = "英语",

isbn = "9781457703065",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE Computer Society",

pages = "1607--1611",

booktitle = "Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011",

address = "美国",

note = "10th International Conference on Machine Learning and Cybernetics, ICMLC 2011 ; Conference date: 10-07-2011 Through 13-07-2011",

}

Fang, K, Wang, DF, Lui, LM, Zhou, SJ, Chu, WCW, Ahuja, AT & Heng, PA 2011, 3D Model-based method for vessel segmentation in TOF-MRA. in Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011., 6016988, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 4, IEEE Computer Society, pp. 1607-1611, 10th International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, Guangxi, China, 10/07/11. https://doi.org/10.1109/ICMLC.2011.6016988

3D Model-based method for vessel segmentation in TOF-MRA. / Fang, Kui; Wang, De Feng; Lui, L. M. et al.
Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011. IEEE Computer Society, 2011. p. 1607-1611 6016988 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 4).

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

TY - GEN

T1 - 3D Model-based method for vessel segmentation in TOF-MRA

AU - Fang, Kui

AU - Wang, De Feng

AU - Lui, L. M.

AU - Zhou, Shou Jun

AU - Chu, W. C.W.

AU - Ahuja, A. T.

AU - Heng, Pheng Ann

PY - 2011

Y1 - 2011

N2 - In this paper, an automatic method to segment the blood vessel for 3D MRA (Magnetic Resonance Angiography) is presented. The segmentation process classifies MRA data into two parts: background and blood vessels. The process includes statistical model based on the voxel intensity and MRF model based on the context information of voxels. Both the models were built on 3D voxel, rather than on 2D. The proposed method is tested on the 3D Time-Of-Flight (TOF)-MRA data. The segmentation results give a good performance in extracting blood vessels.

AB - In this paper, an automatic method to segment the blood vessel for 3D MRA (Magnetic Resonance Angiography) is presented. The segmentation process classifies MRA data into two parts: background and blood vessels. The process includes statistical model based on the voxel intensity and MRF model based on the context information of voxels. Both the models were built on 3D voxel, rather than on 2D. The proposed method is tested on the 3D Time-Of-Flight (TOF)-MRA data. The segmentation results give a good performance in extracting blood vessels.

KW - Context information

KW - Markov random field

KW - Statistical model

KW - Vessel segmentation

UR - https://www.scopus.com/pages/publications/80155206896

U2 - 10.1109/ICMLC.2011.6016988

DO - 10.1109/ICMLC.2011.6016988

M3 - 会议稿件

AN - SCOPUS:80155206896

SN - 9781457703065

T3 - Proceedings - International Conference on Machine Learning and Cybernetics

SP - 1607

EP - 1611

BT - Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011

PB - IEEE Computer Society

T2 - 10th International Conference on Machine Learning and Cybernetics, ICMLC 2011

Y2 - 10 July 2011 through 13 July 2011

ER -

Fang K, Wang DF, Lui LM, Zhou SJ, Chu WCW, Ahuja AT et al. 3D Model-based method for vessel segmentation in TOF-MRA. In Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011. IEEE Computer Society. 2011. p. 1607-1611. 6016988. (Proceedings - International Conference on Machine Learning and Cybernetics). doi: 10.1109/ICMLC.2011.6016988

3D Model-based method for vessel segmentation in TOF-MRA

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Keywords

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