July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Characterizing OCTA Images with Gray Level Co-occurrence Matrix (GLCM) Statistics and Their Correlations to Capillary Density
Author Affiliations & Notes
  • Toco Yuen Ping Chui
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, New York, United States
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Jorge Santiago Andrade Romo
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, New York, United States
  • Giselle Lynch
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, New York, United States
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Amir Zakik
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, New York, United States
  • Rachel E Linderman
    Cell Biology, Neurology and Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
  • Joseph Carroll
    Ophthalmology & Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
    Cell Biology, Neurology and Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
  • Richard B Rosen
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, New York, United States
    Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Footnotes
    Commercial Relationships   Toco Chui, None; Jorge Andrade Romo, None; Giselle Lynch, None; Amir Zakik, None; Rachel Linderman, None; Joseph Carroll, Optovue (F); Richard Rosen, Astellas (C), Bayer (C), Boehringer-Ingelheim (C), Genentech-Roche (C), GlaucoHealth (I), Guardion (I), NanoRetina (C), OD-OS (C), Opticology (I), Optovue (C), Regeneron (C)
  • Footnotes
    Support  This study was supported by the National Eye Institute of the National Institutes of Health under award numbers R01EY027301, R01EY024969 and P30EY001931. Additional funding for this research was provided by the Marrus Family Foundation, the Geraldine Violett Foundation and The Edward N. & Della L. Thome Memorial Foundation.
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 1667. doi:
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      Toco Yuen Ping Chui, Jorge Santiago Andrade Romo, Giselle Lynch, Amir Zakik, Rachel E Linderman, Joseph Carroll, Richard B Rosen; Characterizing OCTA Images with Gray Level Co-occurrence Matrix (GLCM) Statistics and Their Correlations to Capillary Density. Invest. Ophthalmol. Vis. Sci. 2018;59(9):1667.

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

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Abstract

Purpose : Gray level co-occurrence matrix (GLCM) is a statistical analysis approach examining the spatial relations between pixels within an image. This approach provides information about the texture of an image. Using GLCM statistics, we characterized OCTA images in healthy subjects and patients with various stages of diabetic retinopathy and investigated their correlation with capillary density.

Methods : 30 controls and 75 diabetic eyes (30 no retinopathy - NoDR; 22 nonproliferative diabetic retinopathy - NPDR; and 23 proliferative diabetic retinopathy - PDR) were imaged using a commercial SDOCT system (Avanti RTVue-XR; Optovue). A 3x3 mm macular OCTA scan was obtained for each subject. Individual axial length was obtained for the correction of retinal magnification. Full vascular layer OCTA which included both large blood vessels and capillaries located between the inner limiting membrane and 70-µm below the posterior boundary of the inner plexiform layer, was used for image processing and analysis. Capillary images were created after the removal of large blood vessels on the full vascularlayer OCTA. Capillary images were then characterized using GLCM statistics using Matlab (The MathWorks Inc., Natick, MA). GLCM was computed by analyzing pairs of horizontally adjacent pixels with the OCTA image intensity scaled to 8 gray levels. Correlation between GLCM metrics (contrast, correlation, energy, and homogeneity) and capillary density (%) was performed using Pearson’s correlation analysis.

Results : As GLCM-contrast increased significantly (p<0.0001, r=0.87) with increasing capillary density, GLCM-energy (p<0.0001, r=0.90) and GLCM-homogeneity (p<0.0001, r=0.92) decreased significantly with increasing capillary density. There was no significant correlation observed between GLCM-correlation and capillary density (p=0.1, r=0.16).

Conclusions : GLCM statistics provide an alternative approach for direct and quantitative assessments of OCTA, which may reveal additional features of clinical images. Further exploration of their potential application for diagnostic imaging is warranted.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

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