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- Publisher Website: 10.1109/NER.2009.5109279
- Scopus: eid_2-s2.0-70350223651
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Conference Paper: High-resolution reconstruction of human brain MRI image based on local polynomial regression
Title | High-resolution reconstruction of human brain MRI image based on local polynomial regression |
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Authors | |
Keywords | Adaptive scale selection Image reconstruction Local polynomial regression MRI |
Issue Date | 2009 |
Citation | The 4th International IEEE/EMBS Conference on Neural Engineering (NER '09), Antalya, Turkey, 29 April-2 May 2009. In Conference Proceedings, 2009, p. 245-248 How to Cite? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/143321 |
References |
DC Field | Value | Language |
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dc.contributor.author | Zhang, ZG | en_HK |
dc.contributor.author | Chan, SC | en_HK |
dc.contributor.author | Zhang, X | en_HK |
dc.contributor.author | Lam, EY | en_HK |
dc.contributor.author | Wu, EX | en_HK |
dc.contributor.author | Hu, Y | en_HK |
dc.date.accessioned | 2011-11-22T08:30:15Z | - |
dc.date.available | 2011-11-22T08:30:15Z | - |
dc.date.issued | 2009 | en_HK |
dc.identifier.citation | The 4th International IEEE/EMBS Conference on Neural Engineering (NER '09), Antalya, Turkey, 29 April-2 May 2009. In Conference Proceedings, 2009, p. 245-248 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/143321 | - |
dc.description.abstract | This 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.language | eng | en_US |
dc.relation.ispartof | Proceedings of the 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 | en_HK |
dc.subject | Adaptive scale selection | en_HK |
dc.subject | Image reconstruction | en_HK |
dc.subject | Local polynomial regression | en_HK |
dc.subject | MRI | en_HK |
dc.title | High-resolution reconstruction of human brain MRI image based on local polynomial regression | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Zhang, ZG:zgzhang@eee.hku.hk | en_HK |
dc.identifier.email | Chan, SC:scchan@eee.hku.hk | en_HK |
dc.identifier.email | Lam, EY:elam@eee.hku.hk | en_HK |
dc.identifier.email | Wu, EX:ewu1@hkucc.hku.hk | en_HK |
dc.identifier.email | Hu, Y:yhud@hku.hk | en_HK |
dc.identifier.authority | Zhang, ZG=rp01565 | en_HK |
dc.identifier.authority | Chan, SC=rp00094 | en_HK |
dc.identifier.authority | Lam, EY=rp00131 | en_HK |
dc.identifier.authority | Wu, EX=rp00193 | en_HK |
dc.identifier.authority | Hu, Y=rp00432 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/NER.2009.5109279 | en_HK |
dc.identifier.scopus | eid_2-s2.0-70350223651 | en_HK |
dc.identifier.hkuros | 158745 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-70350223651&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 245 | en_HK |
dc.identifier.epage | 248 | en_HK |
dc.identifier.scopusauthorid | Zhang, ZG=8597618700 | en_HK |
dc.identifier.scopusauthorid | Chan, SC=13310287100 | en_HK |
dc.identifier.scopusauthorid | Zhang, X=7410271827 | en_HK |
dc.identifier.scopusauthorid | Lam, EY=7102890004 | en_HK |
dc.identifier.scopusauthorid | Wu, EX=7202128034 | en_HK |
dc.identifier.scopusauthorid | Hu, Y=7407116091 | en_HK |
dc.customcontrol.immutable | sml 170512 amended | - |