Speckle Reduction in Optical Coherence Tomography via Super-Resolution Reconstruction

Rui Zhao, Yitian Zhao, Zhili Chen, Yifan Zhao, Jianlong Yang, Yan Hu, Jun Cheng, Jiang Liu

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

3 Citations (Scopus)

Abstract

Reducing speckle noise from the optical coherence tomograms (OCT) of human retina is a fundamental step to a better visualization and analysis in retinal imaging, as thus to support examination, diagnosis and treatment of many eye diseases. In this study, we propose a new method for speckle reduction in OCT images using the super-resolution technology. It merges multiple images for the same scene but with sub-pixel movements and restores the missing signals in one pixel, which significantly improves the image quality. The proposed method is evaluated on a dataset of 20 OCT volumes (5120 images), through the mean square error, peak signal to noise ratio and the mean structure similarity index using high quality line-scan images as reference. The experimental results show that the proposed method outperforms existing state-of-the-art approaches in applicability, effectiveness, and accuracy.

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5589-5592
Number of pages4
ISBN (Electronic)9781538613115
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: 23 Jul 201927 Jul 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/TerritoryGermany
CityBerlin
Period23/07/1927/07/19

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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