Details

Title

Denoising methods for improving automatic segmentation in OCT images of human eye

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences

Yearbook

2017

Numer

No 1

Publication authors

Keywords

General Engineering ; Computer Networks and Communications ; Atomic and Molecular Physics, and Optics ; Artificial Intelligence ; Information Systems

Divisions of PAS

Nauki Techniczne

Abstract

<jats:title>Abstract</jats:title><jats:p>This paper presents analysis of selected noise reduction methods used in optical coherence tomography (OCT) retina images (the socalled B-scans). The tested algorithms include median and averaging filtering, anisotropic diffusion, soft wavelet thresholding, and multiframe wavelet thresholding. Precision of the denoising process was evaluated based on the results of automated retina layers segmentation, since this stage (vital for ophthalmic diagnosis) is strongly dependent on the image quality. Experiments were conducted with a set of 3D low quality scans obtained from 10 healthy patients and 10 patients with vitreoretinal pathologies. Influence of each method on the automatic image segmentation for both groups of patients is thoroughly described. Manual annotations of investigated retina layers provided by ophthalmology experts served as reference data for evaluation of the segmentation algorithm.</jats:p>

Publisher

Polish Academy of Sciences

Date

2017

Identifier

ISSN 0239-7528, eISSN 2300-1917

DOI

10.1515/bpasts-2017-0009

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