Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

Zhuo Wang, Acner Camino, Miao Zhang, Jie Wang, Thomas Hwang, David Wilson, David Huang, Dengwang Li, Jia Yali

Research output: Contribution to journalArticle

5 Scopus citations

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 languageEnglish (US)
Article number#307477
Pages (from-to)5384-5398
Number of pages15
JournalBiomedical Optics Express
Volume8
Issue number12
DOIs
StatePublished - Dec 1 2017

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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