Coupled parametric model for estimation of visual field tests based on OCT macular thickness maps, and vice versa, in glaucoma care

The current standard of care for glaucoma patients consists of functional assessment of vision via visual field (VF) testing, which is sensitive but subjective, time-consuming, and often unreliable. A new imaging technology, Fourier domain optical coherence tomography (OCT), is being introduced to assess the structural characteristics of the macula. This new complementary exam is efficient, objective, and reliable. A complex, but consistent, relationship exists between the structural information provided by macular OCT and the functional information gathered by VF maps. We propose a learning-based framework with the ability to estimate the VF map based on OCT macular thickness measurements as input (and vice versa). Central to this algorithmic framework is a coupled parametric model that captures not only the individual variabilities of the OCT macular thickness measurements and the VF maps, but also their co-dependencies. This model is derived by applying principal component analysis (PCA) to a library consisting of various pairs of OCT and VF maps. The parameters of this coupled model are obtained by solving a linear least squares problem. Next, these estimated parameters are used, in conjunction with the eigenvectors derived from PCA, to compute the estimate. The accuracy of this coupled parametric estimation model was evaluated by performing multiple leave-one-out cross validation experiments.

Exudate-based diabetic macular edema detection in fundus images using publicly available datasets

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4s (9.3s, considering the optic nerve localisation) per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

In vivo chromium-enhanced MRI of the retina

Chromium (Cr) has been used histologically to stabilize lipid fractions in the retina and is suggested to enhance oxidizable lipids in brain MRI. This study explored the feasibility, sensitivity, and specificity of in vivo chromium-enhanced MRI of retinal lipids by determining its spatiotemporal profiles and toxic effect after intravitreal Cr(VI) injection to normal adult rats. One day after 3 μL Cr(VI) administration at 1-100 mM, the retina exhibited a dose-dependent increase in T1-weighted hyperintensity until 50 mM. Time-dependently, significant T1-weighted hyperintensity persisted up to 2 weeks after 10 mM Cr(VI) administration. Three-dimensional chromium-enhanced MRI of ex vivo normal eyes at isotropic 50-μm resolution showed at least five alternating bands across retinal layers, with the outermost layer being the brightest. This agreed with histology indicating alternating lipid contents with the highest level in the photoreceptor layer of the outer retina. Although Cr(VI) reduction may induce oxidative stress and depolymerize microtubules, manganese-enhanced MRI after chromium-enhanced MRI showed a dose-dependent effect of Cr toxicity on manganese uptake and axonal transport along the visual pathway. These results potentiated future longitudinal chromium-enhanced MRI studies on retinal lipid metabolism upon further optimization of Cr doses with visual cell viability.

Automatic montage of SD-OCT data sets

This paper proposes an automatic algorithm for the montage of OCT data sets, which produces a composite 3D OCT image over a large field of view out of several separate, partially overlapping OCT data sets. First the OCT fundus images (OFIs) are registered, using blood vessel ridges as the feature of interest and a two step iterative procedure to minimize the distance between all matching point pairs over the set of OFIs. Then the OCT data sets are merged to form a full 3D montage using cross-correlation. The algorithm was tested using an imaging protocol consisting of 8 OCT images for each eye, overlapping to cover a total retinal region of approximately 50x35 degrees. The results for 3 normal eyes and 3 eyes with retinal degeneration are analyzed, showing registration errors of 1.5±0.3 and 2.0±0.8 pixels respectively.

[Progress of research in retinal image registration]

The retinal image registration has important applications in the processes of auxiliary diagnosis and treatment for a variety of diseases. The retinal image registration can be used to measure the disease process and the therapeutic effect. A variety of retinal image registration techniques have been studied extensively in recent years. However, there are still many problems existing and there are numerous research possibilities. Based on extensive investigation of existing literatures, the present paper analyzes the feature of retinal image and current challenges of retinal image registration, and reviews the transformation models of the retinal image registration technology and the main research algorithms in current retinal image registration, and analyzes the advantages and disadvantages of various types of algorithms. Some research challenges and future developing trends are also discussed.

Comparison of the Retinomax and Palm-AR Auto-Refractors: a pilot study

PURPOSE

To compare the performance of two handheld auto-refractors, the Retinomax and the Palm-Automatic Refractometer (Palm-AR), for detecting significant vision disorders in pre-school children.

METHODS

Children attending Philadelphia PreKindergarten Head Start were screened with the Retinomax and Palm-AR and underwent a gold standard eye examination. The results of cycloplegic retinoscopy, cover testing, and visual acuity were used to classify children as having normal vision or one of four conditions: amblyopia, strabismus, significant refractive error, and reduced visual acuity. Pass/fail criteria for each instrument were selected to maximize overall sensitivity (with specificity set at 90% and at 94%) for detecting targeted disorders. Comparisons of sensitivities between the auto-refractors were performed using the exact McNemar test.

RESULTS

Testability was >99% for both instruments. Test time was similar for the two instruments (median 2 min; p=0.10). At 90% specificity, the sensitivity for detection of one or more targeted conditions was 74% for the Palm-AR and 78% for the Retinomax. At 94% specificity, the sensitivity for detection of one or more targeted conditions was 66% for both the Palm-AR and the Retinomax. At 90% specificity, the sensitivity for detecting significant refractive error was 84% for both auto-refractors, and at 94% specificity, the sensitivity was 76% for the Palm AR and 75% for the Retinomax. There were high correlations between the instruments for sphere (r=0.85) and cylinder (r=0.88) power. The mean difference between instruments was -0.13 diopters (D) (95% limit of agreement: -2.28 to 2.02) for sphere, and -0.15 D (95% limit of agreement: -0.89 to 0.59) for cylinder.

CONCLUSIONS

In this pilot study, the Retinomax and Palm-AR appear comparable with respect to testability, sensitivity, and specificity. There was strong agreement in readings of sphere and cylinder indicating that they may perform similarly in a screening setting.

Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced local binary pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class support vector machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC>0.93).

Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.

Vessel boundary delineation on fundus images using graph-based approach

This paper proposes an algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously. First, the simultaneous two-boundary segmentation problem is modeled as a two-slice, 3-D surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. We use the REVIEW database to evaluate diameter measurement performance. The algorithm is robust and estimates the vessel width with subpixel accuracy. The method is used to explore the relationship between the average vessel width and the distance from the optic disc in 600 subjects.

Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images

PURPOSE

To compare thickness measurements between Fourier-domain optical coherence tomography (FD-OCT) and time-domain OCT images analyzed with a custom-built OCT retinal image analysis software (OCTRIMA).

METHODS

Macular mapping (MM) by StratusOCT and MM5 and MM6 scanning protocols by an RTVue-100 FD-OCT device are performed on 11 subjects with no retinal pathology. Retinal thickness (RT) and the thickness of the ganglion cell complex (GCC) obtained with the MM6 protocol are compared for each early treatment diabetic retinopathy study (ETDRS)-like region with corresponding results obtained with OCTRIMA. RT results are compared by analysis of variance with Dunnett post hoc test, while GCC results are compared by paired t-test.

RESULTS

A high correlation is obtained for the RT between OCTRIMA and MM5 and MM6 protocols. In all regions, the StratusOCT provide the lowest RT values (mean difference 43 ± 8 μm compared to OCTRIMA, and 42 ± 14 μm compared to RTVue MM6). All RTVue GCC measurements were significantly thicker (mean difference between 6 and 12 μm) than the GCC measurements of OCTRIMA.

CONCLUSION

High correspondence of RT measurements is obtained not only for RT but also for the segmentation of intraretinal layers between FD-OCT and StratusOCT-derived OCTRIMA analysis. However, a correction factor is required to compensate for OCT-specific differences to make measurements more comparable to any available OCT device.

Evaluation of the grading performance of an ensemble-based microaneurysm detector

In this paper, results of a diabetic retinopathy screening experiment are presented which is based solely on the findings of a microaneurysm detector. For this purpose, an ensemble-based algorithm developed by our research group was used; this provided promising results in our earlier experiments. At its best, the 1200 image of the Messidor database is classified by this detector with a sensitivity of 96%, a specificity of 51% and achieved an AUC of 0.87. As anticipated, larger microaneurysm counts are recognized with higher level of certainty. Therefore, this approach might be expected to have good performance in relation to the severity of the disease.

Adaptive multispectral illumination for retinal microsurgery

It has been shown that excessive white light exposure during retinal microsurgery can induce retinal damage. To address this problem, one can illuminate the retina with a device that alternates between white, and less damaging limited-spectrum light. The surgeon is then presented with a fully colored video by recoloring the limited-spectrum light frames, using information from the white-light frames. To obtain accurately colored images, while reducing phototoxicity, we have developed a novel algorithm that monitors the quality of the recolored images and determines when white light may be substituted by limited-spectrum light. We show qualitatively and quantitatively that our system can provide reliable images using a significantly smaller light dose as compared to other state-of-the-art coloring schemes.

Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve

Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

Simple and objective method for routine detection of the macular pigment xanthophyll

A new simple method for two-dimensional determination of optical density of macular pigment xanthophyll (ODx) in clinical routine is based on a single blue-reflection fundus image. Individual different vignetting is corrected by a shading function. For its construction, nodes are automatically found in structureless image regions. The influence of stray light in elderly crystalline lenses is compensated by a correction function that depends on age. The reproducibility of parameters in a one-wavelength reflection method determined for three subjects (47, 61, and 78 years old) was: maxODx = 6.3%, meanODx = 4.6%, volume = 6%, and area = 6% already before stray-light correction. ODx was comparable in pseudophakic and in an eye with a crystalline lens of the same 11 subjects after stray-light correction. Significant correlation in ODx was found between the one-wavelength reflection method and the two-wavelength autofluorescence method for pseudophakic and cataract eyes of 19 patients suffering from dry age-related macular degeneration (AMD) (R(2) = 0.855). In pseudophakic eyes, maxODx was significantly lower for dry AMD (n = 45) (ODx = 0.491±0.102 ODU) than in eyes with healthy fundus (n = 22) (ODx = 0.615±0.103 ODU) (p = 0.000033). Also in eyes with crystalline lens, maxODx was lower in AMD (n = 125) (ODx = 0.610±0.093 ODU) than in healthy subjects (n = 45) (ODx = 0.674±0.098 ODU) (p = 0.00019). No dependence on age was found in the pseudophakic eyes both of healthy subjects and AMD patients.

Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques

Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper.

In vivo functional retinal optical coherence tomography

An experimental design for noninvasive assessment of neural retinal tissue function with enhanced sensitivity is presented. By matching the response detection to a defined flicker frequency stimulus similar to heterodyne detection, the response signal will be shifted out of the low-frequency noise and the specificity of response detection will be strongly enhanced. Optimal measurement parameters are discussed, such as the function and timing of the response function to a single flash stimulus. The results indicate responses on the order of 200 ms that have been probed with our frequency-encoded approach using 5 Hz flickering. Preliminary results indicate the feasibility of our measurement concept to assess small changes in reflectivity with enhanced sensitivity. A functional tomogram for response localization and quantification is introduced.

General retinal vessel segmentation using regularization-based multiconcavity modeling

Detecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remove dark lesions which have an intensity structure different from the line-shaped vessels in a retina. The locally normalized concavity measure is designed to deal with unevenly distributed noise due to the spherical intensity variation in a retinal image. These concavity measures are combined together according to their statistical distributions to detect vessels in general retinal images. Very encouraging experimental results demonstrate that the proposed method consistently yields the best performance over existing state-of-the-art methods on the abnormal retinas and its accuracy outperforms the human observer, which has not been achieved by any of the state-of-the-art benchmark methods. Most importantly, unlike existing methods, the proposed method shows very attractive performances not only on healthy retinas but also on a mixture of healthy and pathological retinas.

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 microm and 26.71 microm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 microm and 0.6 and 1.76 microm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R(2)>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

Statistics of optical coherence tomography data from human retina

Optical coherence tomography (OCT) has recently become one of the primary methods for noninvasive probing of the human retina. The pseudoimage formed by OCT (the so-called B-scan) varies probabilistically across pixels due to complexities in the measurement technique. Hence, sensitive automatic procedures of diagnosis using OCT may exploit statistical analysis of the spatial distribution of reflectance. In this paper, we perform a statistical study of retinal OCT data. We find that the stretched exponential probability density function can model well the distribution of intensities in OCT pseudoimages. Moreover, we show a small, but significant correlation between neighbor pixels when measuring OCT intensities with pixels of about 5 microm. We then develop a simple joint probability model for the OCT data consistent with known retinal features. This model fits well the stretched exponential distribution of intensities and their spatial correlation. In normal retinas, fit parameters of this model are relatively constant along retinal layers, but varies across layers. However, in retinas with diabetic retinopathy, large spikes of parameter modulation interrupt the constancy within layers, exactly where pathologies are visible. We argue that these results give hope for improvement in statistical pathology-detection methods even when the disease is in its early stages.

Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula

Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions--SEADs (Symptomatic Exudate-Associated Derangements)--in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 +/- 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and--together with an anatomical atlas of normal retinas--can be used for clinically important applications.

Automated volumetric evaluation of stereoscopic disc photography

PURPOSE

To develop a fully automated algorithm (AP) to perform a volumetric measure of the optic disc using conventional stereoscopic optic nerve head (ONH) photographs, and to compare algorithm-produced parameters with manual photogrammetry (MP), scanning laser ophthalmoscope (SLO) and optical coherence tomography (OCT) measurements.

METHODS

One hundred twenty-two stereoscopic optic disc photographs (61 subjects) were analyzed. Disc area, rim area, cup area, cup/disc area ratio, vertical cup/disc ratio, rim volume and cup volume were automatically computed by the algorithm. Latent variable measurement error models were used to assess measurement reproducibility for the four techniques.

RESULTS

AP had better reproducibility for disc area and cup volume and worse reproducibility for cup/disc area ratio and vertical cup/disc ratio, when the measurements were compared to the MP, SLO and OCT methods.

CONCLUSION

AP provides a useful technique for an objective quantitative assessment of 3D ONH structures.

Reliability of stereoscopic nonmydriatic retinography for assessment of diabetic macular edema when performed by endocrinologists

BACKGROUND

Diabetic macular edema (DME) is the main cause of moderate vision loss in type 2 diabetes. Diagnosis is achieved by dilated fundus examination or by measuring retinal thickness. However, it can be identified in nonmydriatic retinography (NMR) with hard exudates as a surrogate marker or macular thickness under stereoscopic vision. To date, few studies have focused on interobserver reliability for DME with this technique.

METHODS

Fity-three type 2 diabetes patients with known diabetic retinopathy were studied. We obtained 182 pairs of stereoscopic retinographs with a nonmydriatic camera. Photographic options were 30 degrees or 45 degrees macula-centered retinal field and spontaneous or pharmacological dilation using tropicamide. An endocrinologist with a minimum of training and another with no specific training in this kind of examination diagnosed the images. DME was assumed if retinal thickness was identified within one disc diameter around the fovea.

RESULTS

The kappa index agreement between both endocrinologists for all the data was 0.16 (P = 0.02). Depending on the photographic options, all the kappa indices were below 0.25, except for the 45 degrees retinal field under spontaneous mydriasis (0.58) where the number of samples analyzed was reduced to 22.

CONCLUSIONS

In our study, endocrinologists with a low level of training did not reach a suitable level of agreement regarding the reliability of stereoscopic NMR as a technique for diagnosing DME. We feel that, as NMR can be performed by various different health providers, it would be advisable to establish generally agreed upon criteria for training staff in this technique.

Detection of the optic nerve head in fundus images of the retina with Gabor filters and phase portrait analysis

We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.

Deformable registration of retinal fluorescein angiogram sequences using vasculature structures

State-of-the-art deformable registration algorithms do not perform as well with FA sequences because they are designed to deal with changes of content appearance (e.g., due to different sensors imaging the same organs) but not with content changes, which occur throughout a FA sequence as different portions or the vascular structure are visible (perfused) in different frames. This paper presents a frame-to-frame registration algorithm for ultra-wide-field-of-view (UWFV) fluorescein angiograms (FA) of the retina, based on deformable alignment of the retinal vasculature structure. Comparative experiments on an initial set of UWFV FAs indicate that, thanks to its specialization, our technique outperforms one of the best state-of-the-art methods for multimodal image registration when dealing with the demanding characteristics of the UWFV FA sequences.

Limiting factors to the OCT axial resolution for in-vivo imaging of human and rodent retina in the 1060 nm wavelength range

A computational model was developed to evaluate the limitations to the highest axial resolution, achievable with ultrahigh resolution optical coherence tomography (UHROCT) in the 1060 nm wavelength region for in-vivo imaging of the human and rodent retina. The model considers parameters such as the wavelength dependent water absorption, the average length of the human and rodent eyes, and the power limitations for the imaging beam as defined in the ANSI standard. A custom-built light source with re-shaped spectrum was used to verify experimentally the results from the computational model. Axial OCT resolution of 4.2 microm and 7.7 microm was measured from a mirror reflection with the custom light source by propagating the imaging beam through water cells with 5 mm and 25 mm thickness, corresponding to the average axial length of the rodent and human eye respectively. Assuming an average refractive index of 1.38 for retinal tissue, the expected axial OCT resolution in the rodent and human retina is 3 microm and 5.7 microm respectively. Retinal tomograms acquired in-vivo from the rat eye with the modified light source show clear visualization of all intraretinal layers, as well as a network of capillaries (approximately 10 microm in diameter) in the inner- and outer plexiform layers of the retina.