File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A new eigen-based clutter filter using the Hankel-SVD approach

TitleA new eigen-based clutter filter using the Hankel-SVD approach
Authors
KeywordsClutter Filter
Color Flow Imaging
Hankel Matrix
Singular Value Decomposition
Issue Date2006
Citation
Proceedings - Ieee Ultrasonics Symposium, 2006, v. 1, p. 1079-1082 How to Cite?
AbstractIn color flow data processing, the eigen-regression filter has shown potential in suppressing slow-time clutter while preserving blood echoes because of its adaptability to the Doppler signal contents. However, this filter is inherently based on the use of multiple slow-time snapshots that are statistically stationary. In this article, we present a new eigen-based clutter filter called the Hankel-SVD filter that does not involve the use of multiple slow-time snapshots in its formulation. The new filter, which is derived using the notion of principal Hankel component analysis, works by exploiting the eigen-space properties of a matrix form known as the Hankel matrix. To assess its efficacy, the HankelSVD filter was applied to synthesized slow-time data with arterial flow parameters and low-velocity flow parameters as well as in vivo color flow imaging data obtained from the carotid arteries of a healthy youth. It was found that the new filter generally has better flow detection performance than the clutter-downmixing filter and a fixed-rank multi-snapshot-based eigen-filter. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158471
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorYu, ACHen_US
dc.contributor.authorCobbold, RSCen_US
dc.date.accessioned2012-08-08T08:59:48Z-
dc.date.available2012-08-08T08:59:48Z-
dc.date.issued2006en_US
dc.identifier.citationProceedings - Ieee Ultrasonics Symposium, 2006, v. 1, p. 1079-1082en_US
dc.identifier.issn1051-0117en_US
dc.identifier.urihttp://hdl.handle.net/10722/158471-
dc.description.abstractIn color flow data processing, the eigen-regression filter has shown potential in suppressing slow-time clutter while preserving blood echoes because of its adaptability to the Doppler signal contents. However, this filter is inherently based on the use of multiple slow-time snapshots that are statistically stationary. In this article, we present a new eigen-based clutter filter called the Hankel-SVD filter that does not involve the use of multiple slow-time snapshots in its formulation. The new filter, which is derived using the notion of principal Hankel component analysis, works by exploiting the eigen-space properties of a matrix form known as the Hankel matrix. To assess its efficacy, the HankelSVD filter was applied to synthesized slow-time data with arterial flow parameters and low-velocity flow parameters as well as in vivo color flow imaging data obtained from the carotid arteries of a healthy youth. It was found that the new filter generally has better flow detection performance than the clutter-downmixing filter and a fixed-rank multi-snapshot-based eigen-filter. © 2006 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofProceedings - IEEE Ultrasonics Symposiumen_US
dc.subjectClutter Filteren_US
dc.subjectColor Flow Imagingen_US
dc.subjectHankel Matrixen_US
dc.subjectSingular Value Decompositionen_US
dc.titleA new eigen-based clutter filter using the Hankel-SVD approachen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, ACH:alfred.yu@hku.hken_US
dc.identifier.authorityYu, ACH=rp00657en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ULTSYM.2006.277en_US
dc.identifier.scopuseid_2-s2.0-34547268336en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547268336&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume1en_US
dc.identifier.spage1079en_US
dc.identifier.epage1082en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridYu, ACH=8699317700en_US
dc.identifier.scopusauthoridCobbold, RSC=7005052711en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats