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QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data

  • Franziska SchirrmacherEmail author
  • Thomas Köhler
  • Lennart Husvogt
  • James G. Fujimoto
  • Joachim Hornegger
  • Andreas K. Maier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Franziska Schirrmacher
    • 1
    Email author
  • Thomas Köhler
    • 1
  • Lennart Husvogt
    • 1
  • James G. Fujimoto
    • 2
  • Joachim Hornegger
    • 1
  • Andreas K. Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.Department of Electrical Engineering and Computer Science and Research Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridgeUSA

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