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.
Glaucoma neuropathy is a major cause of irreversible blindness worldwide. Current models of chronic care will not be able to close the gap of growing prevalence of glaucoma and challenges for access to healthcare services. Teleophthalmology is being developed to close this gap. In order to develop automated techniques for glaucoma detection which can be used in tele-ophthalmology we have developed a large retinal fundus dataset. A de-identified dataset of retinal fundus images for glaucoma analysis (RIGA) was derived from three sources for a total of 750 images. The optic cup and disc boundaries for each image was marked and annotated manually by six experienced ophthalmologists and included the cup to disc (CDR) estimates. Six parameters were extracted and assessed (the disc area and centroid, cup area and centroid, horizontal and vertical cup to disc ratios) among the ophthalmologists. The inter-observer annotations were compared by calculating the standard deviation (SD) for every image between the six ophthalmologists in order to determine if the outliers amongst the six and was used to filter the corresponding images. The data set will be made available to the research community in order to crowd source other analysis from other research groups in order to develop, validate and implement analysis algorithms appropriate for tele-glaucoma assessment. The RIGA dataset can be freely accessed online through University of Michigan, Deep Blue website (doi:10.7302/Z23R0R29).
Segmenting the optic disc (OD) is an important and essential step in creating a frame of reference for diagnosing optic nerve head (ONH) pathology such as glaucoma. Therefore, a reliable OD segmentation technique is necessary for automatic screening of ONH abnormalities. The main contribution of this paper is in presenting a novel OD segmentation algorithm based on applying a level set method on a localized OD image. To prevent the blood vessels from interfering with the level set process, an inpainting technique is applied. The algorithm is evaluated using a new retinal fundus image dataset called RIGA (Retinal Images for Glaucoma Analysis). In the case of low quality images, a double level set is applied in which the first level set is considered to be a localization for the OD. Five hundred and fifty images are used to test the algorithm accuracy as well as its agreement with manual markings by six ophthalmologists. The accuracy of the algorithm in marking the optic disc area and centroid is 83.9%, and the best agreement is observed between the results of the algorithm and manual markings in 379 images.