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People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and intervisit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.

Optical coherence tomography (OCT) yields high-resolution, three-dimensional images of the retina. Reliable segmentation of the retinal layers is necessary for the extraction of clinically useful information. We present a novel segmentation method that operates on attenuation coefficients and incorporates anatomical knowledge about the retina. The attenuation coefficients are derived from in-vivo human retinal OCT data and represent an optical property of the tissue. Then, the layers in the retina are simultaneously segmented via a new flexible coupling approach that exploits the predefined order of the layers. The accuracy of the method was evaluated on 20 peripapillary scans of healthy subjects. Ten of those subjects were imaged again to evaluate the reproducibility. An additional evaluation was performed to examine the robustness of the method on a variety of data: scans of glaucoma patients, macular scans and scans by a two different OCT imaging devices. A very good agreement on all data was found between the manual segmentation performed by a medical doctor and the segmentation obtained by the automatic method. The mean absolute deviation for all interfaces in all data types varied between 1.9 and 8.5 µm (0.5-2.2 pixels). The reproducibility of the automatic method was similar to the reproducibility of the manual segmentation., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: Manual and semi-automatic analyses of images, acquired in vivo by confocal microscopy, are often used to determine the quality of corneal endothelium in the human eye. These procedures are highly time consuming. Here, we present two fully automatic methods to analyze and quantify corneal endothelium imaged by in vivo white light slit-scanning confocal microscopy., Methods: In the first approach, endothelial cell density is estimated with the help of spatial frequency analysis. We evaluate published methods, and propose a new, parameter-free method. In the second approach, based on the stochastic watershed, cells are automatically segmented and the result is used to estimate cell density, polymegathism (cell size variability) and pleomorphism (cell shape variation). We show how to determine optimal values for the three parameters of this algorithm, and compare its results to a semi-automatic delineation by a trained observer., Results: The frequency analysis method proposed here is more precise than any published method. The segmentation method outperforms the fully automatic method in the NAVIS software (Nidek Technologies Srl, Padova, Italy), which significantly overestimates the number of cells for cell densities below approximately 1200 mm(-2), as well as previously published methods., Conclusions: The methods presented here provide a significant improvement over the state of the art, and make in vivo, automated assessment of corneal endothelium more accessible. The segmentation method proposed paves the way to many possible new morphometric parameters, which can quickly and precisely be determined from the segmented image.

Purpose: We evaluated the accuracy of a recently developed fundus image registration method (Weighted Vasculature Registration, or WeVaR) and compared it to two top-ranked state-of-the-art commercial fundus mosaicking programs (i2k Retina, DualAlign LLC, and Merge Eye Care PACS, formerly named OIS AutoMontage) in the context of diabetic retinopathy (DR) screening., Methods: Fundus images of 70 diabetic patients who visited the Rotterdam Eye Hospital in 2012 and 2013 for a DR screening program were registered by all three programs. The registration results were used to produce mosaics from fundus photos that were normalized for luminance and contrast to improve the visibility of small details. These mosaics subsequently were evaluated and ranked by two expert graders to assess the registration accuracy., Results: Merge Eye Care PACS had high registration failure rates compared to WeVaR and i2k Retina (P = 8 × 10(-6) and P = 0.002, respectively). WeVaR showed significantly higher registration accuracy than i2k Retina in intravisit (P ≤ 0.0036) and intervisit (P ≤ 0.0002) mosaics. Therefore, fundus mosaics processed by WeVaR were more likely to have a higher score (odds ratio [OR] = 2.5, P = 10(-5) for intravisit and OR = 2.2, P = 0.006 for intervisit mosaics). WeVaR was preferred more often by the graders than i2k Retina (OR = 6.1, P = 7 × 10(-6))., Conclusions: WeVaR produced intra- and intervisit fundus mosaics with higher registration accuracy than Merge Eye Care PACS and i2k Retina. Merge Eye Care PACS had higher registration failures than the other two programs. Highly accurate registration methods, such as WeVaR, may potentially be used for more efficient human grading and in computer-aided screening systems for detecting DR progression., (Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.)

Monitoring glaucoma patients and ensuring optimal treatment requires accurate and precise detection of progression. Many glaucomatous progression detection strategies may be formulated for Scanning Laser Polarimetry (SLP) data of the local nerve fiber thickness. In this paper, several strategies, all based on repeated GDx VCC SLP measurements, are tested to identify the optimal one for clinical use. The parameters of the methods were adapted to yield a set specificity of 97.5% on real image series. For a fixed sensitivity of 90%, the minimally detectable loss was subsequently determined for both localized and diffuse loss. Due to the large size of the required data set, a previously described simulation method was used for assessing the minimally detectable loss. The optimal strategy was identified and was based on two baseline visits and two follow-up visits, requiring two-out-of-four positive tests. Its associated minimally detectable loss was 5-12 μm, depending on the reproducibility of the measurements., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The development of methods to detect slowly progressing diseases is often hampered by the time-consuming acquisition of a sufficiently large data set. In this paper, a method is presented to model the change in images acquired by scanning laser polarimetry, for the detection of glaucomatous progression. The model is based on image series of 23 healthy eyes and incorporates colored noise, incomplete cornea compensation and masking by the retinal blood vessels. Additionally, two methods for detecting progression, taking either one or two follow-up visits into account, are discussed and tested on these simulated images. Both methods are based on Student's t-tests, morphological operations and anisotropic filtering. The images simulated by the model are visually pleasing, show corresponding statistical properties to the real images and are used to optimize the detection methods. The results show that detecting progression based on two follow-up visits greatly improves the sensitivity without adversely affecting the specificity.