Imaging Systems of Human Eye: A Review

Thermal mapping the eye: A critical review of advances in infrared imaging for disease detection

Infrared thermography (IRT) has become more accessible due to technological advancements, making thermal cameras more affordable. Infrared thermal cameras capture the infrared rays emitted by objects and convert it into temperature representations. IRT has emerged as a promising and non-invasive approach for examining the human eye. Ocular surface temperature assessment based on IRT is vital for the diagnosis and monitoring of various eye conditions like dry eye, diabetic retinopathy, glaucoma, allergic conjunctivitis, and inflammatory diseases. A collective sum of 192 articles was sourced from various databases, and through adherence to the PRISMA guidelines, 29 articles were ultimately chosen for systematic analysis. This systematic review article seeks to provide readers with a thorough understanding of IRT's applications, advantages, limitations, and recent developments in the context of eye examinations. It covers various aspects of IRT-based eye analysis, including image acquisition, processing techniques, ocular surface temperature measurement, three different approaches to identifying abnormalities, and different evaluation metrics used. Our review also delves into recent advancements, particularly the integration of machine learning and deep learning algorithms into IRT-based eye examinations. Our systematic review not only sheds light on the current state of research but also outlines promising future prospects for the integration of infrared thermography in advancing eye health diagnostics and care.

Reliable feature selection for automated angle closure glaucoma mechanism detection

Glaucoma is an eye disease where a loss of vision occurs as a result of progressive optic nerve damage usually associates with high intraocular pressure. A subtype of glaucoma called primary angle-closure glaucoma (PACG) has been observed to be the result of one or more mechanisms such as Pupil block, Plateau iris, Peripheral iris roll, and Lens in the anterior segment of the eye. Reliable features in anterior segment images are important for determining the specific mechanisms involved in PACG. In this paper, first the discriminant features are selected by several feature selection algorithms in the context of PACG detection based on anterior segment optical coherence tomography (AS-OCT) images, and then a novel criteria is proposed to further select more reliable features. Our approach is based on selecting the top-ranked features in each algorithm and its rank combination for selection of the best features. Compared with the features selected by the individual feature selection methods, the features selected by our method achieves the best performance in terms of the accuracy of classification of the four PACG mechanisms by using AdaBoost classifier.

Computer-aided diagnosis of proliferative diabetic retinopathy

Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate improved diagnosis and timely treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided modeling and measurement of the openness of the MTA based on a form of the generalized Hough transform for the detection of parabolas, and to compare it with a method of arcade angle measurement. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 10 normal individuals and 15 patients with PDR. The results using the openness parameters of single- and dual-parabolic models as well as the arcade angle measurements indicate areas under the receiver operating characteristics of A(z)= 0.94, 0.87, and 0.84, respectively. The proposed methods should facilitate improved quantitative analysis of the architecture of the MTA, as well as assist in detection, diagnosis, and improved treatment of PDR.

Digital ocular fundus imaging: a review

Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.