The current health care approach for chronic care, such as glaucoma, has limitations for access to expert care and to meet the growing needs of a larger population of older adults who will develop glaucoma. The computer aided diagnosis system (CAD) shows great promise to fill this gap. Our purpose is to expand the initial fundus dataset called Retinal fundus Images for Glaucoma Analysis (RIGA) to develop collaborative image processing methods to automate quantitative optic nerve assessments from fundus photos. All the subjects were women and enrolled in an IRBMED protocol. The fundus photographs were taken using Digital Retinography System (DRS), which is dedicated for diabetic retinopathy screening. Among initial 245 photos, there were 166 photos that met quality assurance metrics for analysis and serve as RIGA2 dataset. Three glaucoma fellowship trained ophthalmologists performed various tasks on these photos. In addition, the cup to disc ratio (CDR) and the neuroretinal rim thickness for the subjects were assessed by slit lamp biomicroscopy and served as the gold standard measure. This RIGA2 dataset is additional 2D color disc photos resource, and multiple extracted features that serves the research community as a form of crowd sourcing analytical power in the growing teleglaucoma field.

Date Published: 15 March 2019
PDF: 5 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540T (15 March 2019); doi: 10.1117/12.2513018

Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)