H Lemij

The Sensitivity and Specificity of Nerve Fiber Layer Measurements in Glaucoma as Determined With Scanning Laser Polarimetry

PURPOSE To determine the sensitivity and specificity for detecting glaucoma by scanning laser polarimetry and to assess the relation between nerve fiber layer (NFL) measurements and visual field indices. METHODS The peripapillary NFL was divided into four segments: superior, inferior, temporal, and nasal. The mean polarimetric NFL for each segment was calculated out of six selected areas of 256 pixels each. Ratios relative to the nasal segment were determined for the superior and inferior segments. With the use of previously obtained normograms for polarimetric NFL readings, the sensitivity of scanning laser polarimetry was assessed in 200 glaucomatous eyes (155 subjects). The specificity was assessed in a normal population of 150 eyes (150 subjects). The relation between hemifield polarimetric NFL and visual field indices was assessed by linear regression analysis. RESULTS The sensitivity of scanning laser polarimetry was 96% and the specificity was 93%. The correlation between NFL parameters and visual field indices ranged from -0.18 to +0.26. The amount of variation by the linear regression model ranged from 3% to 6%. CONCLUSIONS Although quantitative measurements of the NFL with scanning laser polarimetry relate poorly to visual field indices, the technique seems to be promising for screening populations for glaucoma. Whether measurements of the NFL with scanning laser polarimetry are also sensitive enough to detect change over time requires further study.

Measurement by Nerve Fiber Analyzer of Retinal Nerve Fiber Layer Thickness in Normal Subjects and Patients With Ocular Hypertension

PURPOSE To measure retinal nerve fiber layer (NFL) thickness in normal subjects and patients with ocular hypertension and examine the relationship between age and normal NFL thickness. METHODS Nerve fiber layer thickness was determined by scanning laser polarimetry in 210 normal subjects and 100 patients with ocular hypertension. Relative ratios for the superior and inferior NFLs were calculated by dividing the NFL values of the respective regions by the nasal value. RESULTS Mean superior NFL in normal subjects measured 2.5 (95% confidence interval [CI], 1.3 to 3.7), and mean inferior NFL, 2.4 (95% CI, 1.2 to 3.6). Regression analysis showed a gradual decrease in NFL thickness with increasing age. In the patients with ocular hypertension, mean superior and inferior NFL were significantly lower compared with those of normal subjects: superior, 1.6 (95% CI, 0.4 to 2.8) and inferior, 1.6 (95% CI, 1.0 to 2.2). Of the patients with ocular hypertension, 58 of 100 (58%) had an abnormal NFL parameter. CONCLUSIONS Normograms we obtained for NFL as determined by scanning laser polarimetry may serve as reference points for future studies. Patients with ocular hypertension had a significantly lower NFL thickness, although there was some overlap in resulting measurements with those of normal subjects. The Nerve Fiber Analyzer may be useful for individual follow-up of people at risk for glaucoma; however, its role as a screening instrument requires further study.

A model based method for retinal blood vessel detection

Retinal blood vessels are important structures in ophthalmological images. Many detection methods are available, but the results are not always satisfactory. In this paper, we present a novel model based method for blood vessel detection in retinal images. It is based on a Laplace and thresholding segmentation step, followed by a classification step to improve performance. The last step assures incorporation of the inner part of large vessels with specular reflection. The method gives a sensitivity of 92% with a specificity of 91%. The method can be optimized for the specific properties of the blood vessels in the image and it allows for detection of vessels that appear to be split due to specular reflection.

Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography

We present a method, based on a single scattering model, to calculate the attenuation coefficient of each pixel in optical coherence tomography (OCT) depth profiles. Numerical simulations were used to determine the models response to different depths and attenuation coefficients. Experiments were performed on uniform and layered phantoms with varying attenuation coefficients. They were measured by a 1300 nm OCT system and their attenuation coefficients were evaluated by our proposed method and by fitting the OCT slope as the gold standard. Both methods showed largely consistent results for the uniform phantoms. On the layered phantom, only our proposed method accurately estimated the attenuation coefficients. For all phantoms, the proposed method largely reduced the variability of the estimated attenuation coefficients. The method was illustrated on an in-vivo retinal OCT scan, effectively removing common imaging artifacts such as shadowing. By providing localized, per-pixel attenuation coefficients, this method enables tissue characterization based on attenuation coefficient estimates from OCT data.

Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images

Current OCT devices provide three-dimensional (3D) in-vivo images of the human retina. The resulting very large data sets are difficult to manually assess. Automated segmentation is required to automatically process the data and produce images that are clinically useful and easy to interpret. In this paper, we present a method to segment the retinal layers in these images. Instead of using complex heuristics to define each layer, simple features are defined and machine learning classifiers are trained based on manually labeled examples. When applied to new data, these classifiers produce labels for every pixel. After regularization of the 3D labeled volume to produce a surface, this results in consistent, three-dimensionally segmented layers that match known retinal morphology. Six labels were defined, corresponding to the following layers: Vitreous, retinal nerve fiber layer (RNFL), ganglion cell layer & inner plexiform layer, inner nuclear layer & outer plexiform layer, photoreceptors & retinal pigment epithelium and choroid. For both normal and glaucomatous eyes that were imaged with a Spectralis (Heidelberg Engineering) OCT system, the five resulting interfaces were compared between automatic and manual segmentation. RMS errors for the top and bottom of the retina were between 4 and 6 μm, while the errors for intra-retinal interfaces were between 6 and 15 μm. The resulting total retinal thickness maps corresponded with known retinal morphology. RNFL thickness maps were compared to GDx (Carl Zeiss Meditec) thickness maps. Both maps were mostly consistent but local defects were better visualized in OCT-derived thickness maps.

The Effect of Glaucoma on the Optical Attenuation Coefficient of the Retinal Nerve Fiber Layer in Spectral Domain Optical Coherence Tomography Images

PURPOSE To demonstrate the effect of glaucoma on the optical attenuation coefficient of the retinal nerve fiber layer (RNFL) in Spectral Domain Optical Coherence Tomography (SD-OCT) images. METHODS We analyzed images of the peripapillary areas in 10 healthy and 30 glaucomatous eyes (mild, moderate, and advanced glaucoma, 10 eyes each), scanned with the Spectralis OCT (Heidelberg Engineering GmbH, Dossenheim, Germany). To calculate the RNFL attenuation coefficient (μ(att)), determined by the scattering properties of the RNFL, we used a model that normalized the reflectivity of the RNFL by the retinal pigment epithelium. The analysis was performed at four preset locations at 1.3 and 1.7 mm from the center of the optic nerve head (ONH) (i.e., temporally, superiorly, nasally, and inferiorly) and on averages per eye. To assess the structure-function relationship, we correlated the μ(att) to the mean deviation (MD) in standard automated perimetry. RESULTS The μ(att) of the RNFL decreased up to 40% with increasing disease severity, on average as well as in each location around the ONH (Jonckheere-Terpstra test, P < 0.019 in all tests). The μ(att) of the RNFL depended significantly on the location around the ONH in all eyes (Kruskal-Wallis test, P < 0.014) and was lowest nasally from the ONH. The μ(att) correlated significantly with the MD in SAP (R(2) = 0.337). CONCLUSIONS The measurements clearly demonstrated that the μ(att) of the RNFL decreased with increasing disease severity. The RNFL attenuation coefficient may serve as a new method to quantify glaucoma in SD-OCT images.

RPE-normalized RNFL attenuation coefficient maps derived from volumetric OCT imaging for glaucoma assessment.

PURPOSE We present spatial retinal nerve fiber layer (RNFL) attenuation coefficient maps for healthy and glaucomatous eyes based on optical coherence tomography (OCT) measurements. Quantitative analyses of differences between healthy and glaucomatous eyes were performed. METHODS Peripapillary volumetric images of 10 healthy and 8 glaucomatous eyes were acquired by a Spectralis OCT system. Per A-line, the attenuation coefficient of the RNFL was determined based on a method that uses the retinal pigment epithelium as a reference layer. The attenuation coefficient describes the attenuation of light in tissue due to scattering and absorption. En-face maps were constructed and visually inspected. Differences between healthy and glaucomatous eyes were analyzed (Mann-Whitney U test), both globally (average values) and spatially (concentric and per segment). RESULTS RNFL attenuation coefficient maps of healthy eyes showed relatively high and uniform values. For glaucomatous eyes, the attenuation coefficients were much lower and showed local defects. Normal and glaucomatous average RNFL attenuation coefficients were highly significantly different (P < 0.0001) and fully separable. The RNFL attenuation coefficient decreased with increasing optic nerve head distance for both groups, with highly significant differences for all distances (P < 0.001). The angular dependency showed high superio- and inferiotemporal and low nasal values, with most significant differences superio- and inferiotemporally. CONCLUSIONS Maps of RNFL attenuation coefficients provide a novel way of assessing the health of the RNFL and are relatively insensitive to imaging artifacts affecting signal intensity. The highly significant difference between normal and glaucomatous eyes suggests using RNFL attenuation coefficient maps as a new clinical tool for diagnosing and monitoring glaucoma.

Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography

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.

Modeling of scanning laser polarimetry images of the human retina for progression detection of glaucoma

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 Students 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.

Is Hyperopia an Important Risk Factor for PACG in the Dutch Population?—A Case Control Study

Objectives. To determine if hyperopia is a risk factor for primary angle-closure glaucoma (PACG) in the Dutch population and to identify other biometrical parameters as risk factors for PACG including axial length (AL), anterior chamber depth (ACD), and k values. Methods. The study population consisted of PACG patients that had undergone a laser peripheral iridotomy (LPI). The control group consisted of age- and gender-matched cataract patients. The main outcome was hyperopia (spherical equivalent ≥+0.5 dioptres) measured with IOL Master or autorefractor. Refractive error, ACD, AL, and k values were tested with a Mann-Whitney U test and by logistic regression. Results. 117 PACG patients and 234 controls were included (mean age = 80 years ± 3.6). The prevalence of hyperopia in patients and controls was 69.6% and 61.1%, respectively (Fishers test P = 0.076). Mann-Whitney U test showed no statistically significant relation with refractive error (P = 0.068) or k values (P = 0.607). In contrast, ACD and AL were statistically significant (P < 0.001). Tested with logistic regression, only ACD was a significant predictor of PACG (P < 0.001). Conclusion. There was no statistically significant correlation between refractive error and PACG. ACD was strongly correlated, though, with PACG, whereas AL turned out to be a less significant risk factor.