June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Texture analysis of OCT phantoms
Author Affiliations & Notes
  • Mukhit Kulmaganbetov
    School of Optometry and Vision sciences, Cardiff University, Cardiff, United Kingdom
    Kazakh Eye Research Institute, Almaty, Kazakhstan
  • Nantheera Anantrasirichai
    Visual Information Laboratory, University of Bristol, Bristol, United Kingdom
  • Alin Achim
    Visual Information Laboratory, University of Bristol, Bristol, United Kingdom
  • Julie Albon
    School of Optometry and Vision sciences, Cardiff University, Cardiff, United Kingdom
  • Nick White
    School of Optometry and Vision sciences, Cardiff University, Cardiff, United Kingdom
  • James Edwards Morgan
    School of Optometry and Vision sciences, Cardiff University, Cardiff, United Kingdom
  • Footnotes
    Commercial Relationships   Mukhit Kulmaganbetov, None; Nantheera Anantrasirichai, None; Alin Achim, None; Julie Albon, None; Nick White, None; James Morgan, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 2038. doi:
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      Mukhit Kulmaganbetov, Nantheera Anantrasirichai, Alin Achim, Julie Albon, Nick White, James Edwards Morgan; Texture analysis of OCT phantoms. Invest. Ophthalmol. Vis. Sci. 2020;61(7):2038.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Texture analysis has shown value in optical coherence tomography (OCT) imaging for the detection of early degenerative retinal changes. The calibration of texture-based techniques requires gold standard targets. We, therefore, evaluated a range of retinal imaging phantoms to evaluate techniques for texture-based analysis with OCT.

Methods : Four groups of phantom types were prepared and imaged (Table 1): different concentrations of a medium matrix (gelatin solution); different sized polystyrene beads (PBs); different volume of PBs and different refractive indices of scatterers (PBs and SiO2). The phantom particles were selected to lie in the size range of subcellular organelles that would be expected to change during retinal neuronal degeneration. OCT at 1040nm (bandwidth 70nm) was performed to acquire 3D image datasets (512×512×1024 pixels) of each phantom, using a custom research OCT with a microscope attachment. Five grey-level co-occurrence matrix features (energy, contrast, entropy, correlation and homogeneity) in four directions (0o, 90o, 180o, 270o) were extracted from each random volumes of interest (VOI, n=10, 30×30×30 pixels).

Results : Textural parameters were analysed in 20-dimensional feature space using principal component analysis (PCA) and support vector machine (SVM) written in Matlab. PCA was used to reduce the feature dimensions by eliminating redundant data, using SVM served as a classifier against the known state of the phantom. The classification approaches were tested on the 160 OCT images of our four groups of 16 types of phantoms (Table 2).

Conclusions : We presented a semi-automated method for the classification of OCT images. Even though the OCT scans of the various phantoms are optically similar for unaided eye, they can be discriminated using OCT-based texture analysis.

This is a 2020 ARVO Annual Meeting abstract.

 

Table 1. Groups and types of phantoms

Table 1. Groups and types of phantoms

 

Table 2. Classification accuracy of different phantom groups

Table 2. Classification accuracy of different phantom groups

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