Abstract
Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.
Original language | English (US) |
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Article number | #307477 |
Pages (from-to) | 5384-5398 |
Number of pages | 15 |
Journal | Biomedical Optics Express |
Volume | 8 |
Issue number | 12 |
DOIs | |
State | Published - Dec 1 2017 |
ASJC Scopus subject areas
- Biotechnology
- Atomic and Molecular Physics, and Optics