Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model

Bing Nan Li; Chee Kong Chui; Sim Heng Ong; Toshikatsu Washio; Tomokazu Numano; Stephen Chang; Sudhakar Venkatesh; Etsuko Kobayashi

doi:10.1007/978-3-642-15948-0_10

Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model

Bing Nan Li, Chee Kong Chui, Sim Heng Ong, Toshikatsu Washio, Tomokazu Numano, Stephen Chang, Sudhakar Venkatesh, Etsuko Kobayashi

Radiology

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

1 Scopus citations

Abstract

Magnetic resonance elastography (MRE) noninvasively images the propagation of mechanical waves within soft tissues. The elastic properties of soft tissues can then be quantified from MRE wave snapshots. Various algorithms have been proposed to obtain their inversion for soft tissue elasticity. Anomalies are assumed to be discernible in the elasticity map. We propose a new elastic level set model to directly detect and track abnormal soft tissues in MRE wave images. It is derived from the Mumford-Shah functional, and employs partial differential equations for function modeling and smoothing. This level set model can interpret MRE wave images without elasticity reconstruction. The experimental results on synthetic and real MRE wave images confirm its effectiveness for soft tissue discrimination.

Original language	English (US)
Title of host publication	Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings
Pages	76-83
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-15948-0_10
State	Published - 2010
Event	1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010 - Beijing, China Duration: Sep 20 2010 → Sep 20 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6357 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010
Country/Territory	China
City	Beijing
Period	9/20/10 → 9/20/10

Keywords

Elastic imaging
level set methods
magnetic resonance elastography (MRE)
medical image segmentation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-15948-0_10

Cite this

Li, B. N., Chui, C. K., Ong, S. H., Washio, T., Numano, T., Chang, S., Venkatesh, S., & Kobayashi, E. (2010). Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model. In Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings (pp. 76-83). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6357 LNCS). https://doi.org/10.1007/978-3-642-15948-0_10

Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model. / Li, Bing Nan; Chui, Chee Kong; Ong, Sim Heng et al.
Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings. 2010. p. 76-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6357 LNCS).

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

Li, BN, Chui, CK, Ong, SH, Washio, T, Numano, T, Chang, S, Venkatesh, S & Kobayashi, E 2010, Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model. in Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6357 LNCS, pp. 76-83, 1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, 9/20/10. https://doi.org/10.1007/978-3-642-15948-0_10

Li BN, Chui CK, Ong SH, Washio T, Numano T, Chang S et al. Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model. In Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings. 2010. p. 76-83. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15948-0_10

Li, Bing Nan ; Chui, Chee Kong ; Ong, Sim Heng et al. / Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model. Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings. 2010. pp. 76-83 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a59ca3c00af2453abd969fd3186095c3,

title = "Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model",

abstract = "Magnetic resonance elastography (MRE) noninvasively images the propagation of mechanical waves within soft tissues. The elastic properties of soft tissues can then be quantified from MRE wave snapshots. Various algorithms have been proposed to obtain their inversion for soft tissue elasticity. Anomalies are assumed to be discernible in the elasticity map. We propose a new elastic level set model to directly detect and track abnormal soft tissues in MRE wave images. It is derived from the Mumford-Shah functional, and employs partial differential equations for function modeling and smoothing. This level set model can interpret MRE wave images without elasticity reconstruction. The experimental results on synthetic and real MRE wave images confirm its effectiveness for soft tissue discrimination.",

keywords = "Elastic imaging, level set methods, magnetic resonance elastography (MRE), medical image segmentation",

author = "Li, {Bing Nan} and Chui, {Chee Kong} and Ong, {Sim Heng} and Toshikatsu Washio and Tomokazu Numano and Stephen Chang and Sudhakar Venkatesh and Etsuko Kobayashi",

year = "2010",

doi = "10.1007/978-3-642-15948-0_10",

language = "English (US)",

isbn = "3642159478",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "76--83",

booktitle = "Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings",

note = "1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010 ; Conference date: 20-09-2010 Through 20-09-2010",

}

TY - GEN

T1 - Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model

AU - Li, Bing Nan

AU - Chui, Chee Kong

AU - Ong, Sim Heng

AU - Washio, Toshikatsu

AU - Numano, Tomokazu

AU - Chang, Stephen

AU - Venkatesh, Sudhakar

AU - Kobayashi, Etsuko

PY - 2010

Y1 - 2010

N2 - Magnetic resonance elastography (MRE) noninvasively images the propagation of mechanical waves within soft tissues. The elastic properties of soft tissues can then be quantified from MRE wave snapshots. Various algorithms have been proposed to obtain their inversion for soft tissue elasticity. Anomalies are assumed to be discernible in the elasticity map. We propose a new elastic level set model to directly detect and track abnormal soft tissues in MRE wave images. It is derived from the Mumford-Shah functional, and employs partial differential equations for function modeling and smoothing. This level set model can interpret MRE wave images without elasticity reconstruction. The experimental results on synthetic and real MRE wave images confirm its effectiveness for soft tissue discrimination.

AB - Magnetic resonance elastography (MRE) noninvasively images the propagation of mechanical waves within soft tissues. The elastic properties of soft tissues can then be quantified from MRE wave snapshots. Various algorithms have been proposed to obtain their inversion for soft tissue elasticity. Anomalies are assumed to be discernible in the elasticity map. We propose a new elastic level set model to directly detect and track abnormal soft tissues in MRE wave images. It is derived from the Mumford-Shah functional, and employs partial differential equations for function modeling and smoothing. This level set model can interpret MRE wave images without elasticity reconstruction. The experimental results on synthetic and real MRE wave images confirm its effectiveness for soft tissue discrimination.

KW - Elastic imaging

KW - level set methods

KW - magnetic resonance elastography (MRE)

KW - medical image segmentation

UR - http://www.scopus.com/inward/record.url?scp=77958025767&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77958025767&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15948-0_10

DO - 10.1007/978-3-642-15948-0_10

M3 - Conference contribution

AN - SCOPUS:77958025767

SN - 3642159478

SN - 9783642159473

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 76

EP - 83

BT - Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings

T2 - 1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010

Y2 - 20 September 2010 through 20 September 2010

ER -

Soft tissue discrimination using magnetic resonance elastography with a new elastic level set model

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this