Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

Aspects of structural and functional assessment in open angle glaucoma

Early detection of glaucoma is a prerequisite for effective management of the disease. The study was concerned with aspects of structural and functional assessment in open angle glaucoma. The major part of the study was concerned with the utilization of digital stereoscopic imaging of the optic nerve head in the detection of open angle glaucoma (OAG). Specifically, it addressed possible sources of variability that confound the diagnosis of glaucoma and are associated with the monoscopic, as opposed to stereoscopic, observation of the optic nerve head (ONH) the limited diagnostic value of the features of the peripapillary retina accompanying glaucomatous damage and the between-observer variation in the subjective evaluation of the ONH. The study utilised a dataset of magnification corrected digital images from 51 normal individuals and from 113 patients with OAG. Misdiagnosis of glaucoma was associated with discrepancies in the evaluation of the rim area due to the monoscopic presentation of the ONH masking the presence of focal rim loss, otherwise evident with stereoscopic observation. The frequency and patterns of distribution of the alpha and beta peripapillary atrophy (PPA) were confirmed among normal and glaucomatous eyes but meaningful conclusions on the diagnostic value of PPA were hindered by the clinically broad criteria of this feature. Regression analysis of the global and sectorial rim areas for the discrimination of glaucomatous damage compared favourably with the subjective glaucoma diagnosis by expert observers. The remaining part of the study was concerned with the evaluation of the Total and Pattern Deviation probability analysis in short-wavelength perimetry (SWAP). The material comprised the Humphrey Field Analyzer single field print-outs from standard automated perimetry (SAP) and from SWAP of 53 normal individuals 18 patients with cataract, 22 with OHT and 55 with OAG. Focal visual field loss derived by SWAP was markedly less compared to SWAP indicating wider limits of normality for SWAP. Considerable caution should be exercised before the use of SWAP

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

Actas de las XXXIV Jornadas de Automática

Postprint (published version