1. Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs
- Author
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Mendonça, Ana Maria, Remeseiro, Beatriz, Dashtbozorg, Behdad, Campilho, Aurélio, Petrick, Nicholas A., Armato, Samuel G., and Medical Image Analysis
- Subjects
business.industry ,Computer science ,Computation ,Retinal images ,Retinal ,Diabetic retinopathy ,Image segmentation ,Vessel Segmentation ,SDG 3 – Goede gezondheid en welzijn ,medicine.disease ,Arteriolar-to-Venular Ratio ,030218 nuclear medicine & medical imaging ,Artery/Vein classification ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,chemistry ,SDG 3 - Good Health and Well-being ,Region of interest ,030221 ophthalmology & optometry ,medicine ,Computer vision ,Artificial intelligence ,business ,Reliability (statistics) - Abstract
The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients’ condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.
- Published
- 2017