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

Z. G. Zhang, S. C. Chan, Xixiang Zhang, E. Y. Lam, E. X. Wu, Y. Hu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Pages245-248
Number of pages4
DOIs
StatePublished - Oct 27 2009
Event2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 - Antalya, Turkey
Duration: Apr 29 2009May 2 2009

Publication series

Name2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09

Other

Other2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
CountryTurkey
CityAntalya
Period04/29/0905/2/09

Keywords

  • Adaptive scale selection
  • Image reconstruction
  • Local polynomial regression
  • MRI

ASJC Scopus subject areas

  • Biomedical Engineering
  • Clinical Neurology
  • Neuroscience(all)

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