Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

• Published in: Biomedical optics express Vol. 5; no. 10; pp. 3568 - 3577
• Main Authors: Srinivasan, Pratul P, Kim, Leo A, Mettu, Priyatham S, Cousins, Scott W, Comer, Grant M, Izatt, Joseph A, Farsiu, Sina
• Format: Journal Article
• Language: English
• Published: United States Optical Society of America 01.10.2014
• Subjects: (100.5010) Pattern recognition; (100.2960) Image analysis; (110.4500) Optical coherence tomography; (100.0100) Image processing; (170.4470) Ophthalmology
• ISSN: 2156-7085; 2156-7085
• DOI: 10.1364/boe.5.003568

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases.