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Chapter

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Title

Matching 3D OCT Retina Images into Super-Resolution Dataset

Authors

[ 1 ] Instytut Automatyki i Robotyki, Wydział Informatyki, Politechnika Poznańska | [ P ] employee

Year of publication

2016

Chapter type

paper

Publication language

english

Keywords
EN
  • OCT
  • super-resolution
  • multi-frame
  • retina image segmentation
Abstract

EN Optical coherence tomography (OCT) is the current very fast and accurate modality for noninvasive assessment of 3D retinal structure. Due to large amount of data acquired with this technique the resolution of 3D scans is limited. In this paper we present a new method for improving resolution of 3D macula scans while maintaining short acquisition time and robustness with respect to motion artifacts. Our approach is based on multiframe super-resolution method applied to several 3D standard resolution OCT scans. Presented experiments where performed on volumetric data acquired from adult patients with the use of Avanti RTvue device. Each OCT cross-section (B-scan) was subjected to image denoising and retinal layers segmentation. The generated 3D super-resolution scans have significantly improved quality of the vertical cross-sections.

Pages (from - to)

130 - 137

DOI

10.1109/SPA.2016.7763600

URL

http://ieeexplore.ieee.org/document/7763600/

Book

SPA 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications, Poznań, 21-23 September, 2016 : conference proceeding

Presented on

20th Signal Processing Algorithms, Architectures, Arrangements, and Applications, SPA 2016, 21-23.09.2016, Poznań, Poland

Publication indexed in

WoS (15)

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