The blood-ocular barrier system is formed by 2 main barriers: the blood-aqueous barrier and the blood-retinal barrier (BRB). The BRB is particularly tight and restrictive and is a physiologic barrier that regulates ion, protein, and water flux into and out of the retina. The BRB consists of inner and outer components, the inner BRB being formed of tight junctions between retinal capillary endothelial cells and the outer BRB of tight junctions between retinal pigment epithelial cells. The BRB is essential to maintaining the eye as a privileged site and is essential for normal visual function. Methods of clinical evaluation of the BRB are reviewed and new directions using optical coherence tomography are presented. Alterations of the BRB play a crucial role in the development of retinal diseases. The 2 most frequent and relevant retinal diseases, diabetic retinopathy and age-related macular degeneration (AMD), are directly associated with alterations of the BRB. Diabetic retinopathy is initiated by an alteration of the inner BRB and neovascular AMD is a result of an alteration of the outer BRB. Macular edema is a direct result of alterations of the BRB.

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.

Despeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is now made to be dependent on data, and the process itself is now an adaptive one. Experimental results making use of synthetic data show the good performance of the proposed formulation by achieving better quantitative results and increasing computation speed.

To characterize macular edema that occurs after uneventful cataract surgery. Centre of Ophthalmology, University Hospital, Institute of Biomedical Research on Light and Image, Faculty of Medicine, University of Coimbra, Coimbra, Portugal. Thirty-two eyes of 32 patients had uneventful phacoemulsification with implantation of a foldable intraocular lens. Postoperatively, patients were examined at 3, 6, 12, and 30 weeks. The examinations included retinal leakage analysis (Zeiss CSLO), optical coherence tomography (Humphrey Instruments), and retinal thickness analysis (Talia Technology, Ltd.). Results were compared with those in a control group comprising healthy subjects. Increases in retinal thickness (ie, over the mean +/- 2 SD in the control group) reached a maximum at 6 weeks in 13 of 32 eyes (41%), after which recovery was progressive. At 30 weeks, all eyes had good visual acuity, but 7 eyes (22%) still had macular edema. The edema was located primarily in the central macular region. Leaking sites involving the vascular areas of the macula, which indicated areas of abnormal blood-retinal barrier permeability, were a frequent finding. The number of sites remained relatively stable during the first 12 weeks (88%) and decreased to 68% at 30 weeks, indicating a trend toward recovery. Macular edema after cataract surgery occurred primarily in the central region of the macula and was associated with the presence of leaking sites, which were located predominantly in the vascular regions of the central macula.

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.

A top priority in biomarker development for Alzheimer’s disease (AD) and Parkinson’s disease (PD) is the focus on early diagnosis, where the use of the retina is a promising avenue of research. We computed fundus images from optical coherence tomography (OCT) data and analysed the structural arrangement of the retinal tissue using texture metrics. We built clinical class classification models to distinguish between healthy controls (HC), AD, and PD, using machine learning (support vector machines). Median sensitivity is 88.7%, 79.5% and 77.8%, for HC, AD, and PD eyes, respectively. When the same subject has the same classification for both eyes, 94.4% (median) of the classifications are correct. A significant amount of information discriminating between multiple neurodegenerative states is conveyed by OCT imaging of the human retina, even when differences in thickness are not yet present. This technique may allow for simultaneously diagnosing Alzheimer’s and Parkinson’s diseases.