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Conference Paper: High-resolution reconstruction of human brain MRI image based on local polynomial regression

TitleHigh-resolution reconstruction of human brain MRI image based on local polynomial regression
Authors
KeywordsAdaptive scale selection
Image reconstruction
Local polynomial regression
MRI
Issue Date2009
Citation
2009 4Th International Ieee/Embs Conference On Neural Engineering, Ner '09, 2009, p. 245-248 How to Cite?
AbstractThis paper introduces a new local polynomial regression (LPR)-based high-resolution image reconstruction method for human brain magnetic resonance images. In LPR, the image pixels are modeled locally by a polynomial using least-squares (LS) criterion with a kernel having a certain bandwidth matrix. Steering kernels with local orientation are used in LPR to adapt better to local characteristics of images. Furthermore, a refined intersection of confidence intervals (RICI) adaptive scale selector is adopted to select the scale of the steering kernels. The resulting steering-kernel-based LPR with RICI (SK-LPR-RICI) method is applied to reconstruct a high-resolution brain MRI image from a set of low-resolution MRI images. Simulation results show that the proposed SK-LPR-RICI method can effectively improve the image resolution and peak signal-to-noise ratio. ©2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/143321
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhang, Xen_HK
dc.contributor.authorLam, EYen_HK
dc.contributor.authorWu, EXen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2011-11-22T08:30:15Z-
dc.date.available2011-11-22T08:30:15Z-
dc.date.issued2009en_HK
dc.identifier.citation2009 4Th International Ieee/Embs Conference On Neural Engineering, Ner '09, 2009, p. 245-248en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143321-
dc.description.abstractThis paper introduces a new local polynomial regression (LPR)-based high-resolution image reconstruction method for human brain magnetic resonance images. In LPR, the image pixels are modeled locally by a polynomial using least-squares (LS) criterion with a kernel having a certain bandwidth matrix. Steering kernels with local orientation are used in LPR to adapt better to local characteristics of images. Furthermore, a refined intersection of confidence intervals (RICI) adaptive scale selector is adopted to select the scale of the steering kernels. The resulting steering-kernel-based LPR with RICI (SK-LPR-RICI) method is applied to reconstruct a high-resolution brain MRI image from a set of low-resolution MRI images. Simulation results show that the proposed SK-LPR-RICI method can effectively improve the image resolution and peak signal-to-noise ratio. ©2009 IEEE.en_HK
dc.languageengen_US
dc.relation.ispartof2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09en_HK
dc.subjectAdaptive scale selectionen_HK
dc.subjectImage reconstructionen_HK
dc.subjectLocal polynomial regressionen_HK
dc.subjectMRIen_HK
dc.titleHigh-resolution reconstruction of human brain MRI image based on local polynomial regressionen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailZhang, ZG:zgzhang@eee.hku.hken_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailLam, EY:elam@eee.hku.hken_HK
dc.identifier.emailWu, EX:ewu1@hkucc.hku.hken_HK
dc.identifier.emailHu, Y:yhud@hku.hken_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityLam, EY=rp00131en_HK
dc.identifier.authorityWu, EX=rp00193en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/NER.2009.5109279en_HK
dc.identifier.scopuseid_2-s2.0-70350223651en_HK
dc.identifier.hkuros158745-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70350223651&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage245en_HK
dc.identifier.epage248en_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhang, X=7410271827en_HK
dc.identifier.scopusauthoridLam, EY=7102890004en_HK
dc.identifier.scopusauthoridWu, EX=7202128034en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK

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