Textural feature analysis of optical coherence tomography phantoms

Kulmaganbetov, Mukhit ORCID: https://orcid.org/0000-0003-2112-4208, Bevan, Ryan J. ORCID: https://orcid.org/0000-0002-2557-2887, Anantrasirichai, Nantheera, Achim, Alin, Erchova, Irina, White, Nick, Albon, Julie ORCID: https://orcid.org/0000-0002-3029-8245 and Morgan, James E. 2022. Textural feature analysis of optical coherence tomography phantoms. Electronics 11 (4) , 669. 10.3390/electronics11040669

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Abstract

Optical coherence tomography (OCT) is an imaging technique based on interferometry of backscattered lights from materials and biological samples. For the quantitative evaluation of an OCT system, artificial optical samples or phantoms are commonly used. They mimic the structure of biological tissues and can provide a quality standard for comparison within and across devices. Phantoms contain medium matrix and scattering particles within the dimension range of target biological structures such as the retina. The aim was to determine if changes in speckle derived optical texture could be employed to classify the OCT phantoms based on their structural composition. Four groups of phantom types were prepared and imaged. These comprise different concentrations of a medium matrix (gelatin solution), different sized polystyrene beads (PBs), the volume of PBs and different refractive indices of scatterers (PBs and SiO2). Texture analysis was applied to detect subtle optical differences in OCT image intensity, surface coarseness and brightness of regions of interest. A semi-automated classifier based on principal component analysis (PCA) and support vector machine (SVM) was applied to discriminate the various texture models. The classifier detected correctly different phantom textures from 82% to 100%, demonstrating that analysis of the texture of OCT images can be potentially used to discriminate biological structure based on subtle changes in light scattering.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Optometry and Vision Sciences
Additional Information:	This article belongs to the Special Issue Medical Image Analysis and Computer Vision This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher:	MDPI
ISSN:	2079-9292
Funders:	Vice�Chancellor�s International Scholarship for Research Excellence
Date of First Compliant Deposit:	28 June 2022
Date of Acceptance:	20 February 2022
Last Modified:	05 Jan 2024 03:09
URI:	https://orca.cardiff.ac.uk/id/eprint/150842

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