One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

High speed Optical Coherence Tomography (OCT) has made it possible to rapidly capture densely sampled 3D volume data. One key application is the acquisition of high quality in vivo volumetric data sets of the human retina. Since the volume is acquired in a few seconds, eye movement during the scan process leads to distortion, which limits the accuracy of quantitative measurements using 3D OCT data. In this paper, we present a novel software based method to correct motion artifacts in OCT raster scans. Motion compensation is performed retrospectively using image registration algorithms on the OCT data sets themselves. Multiple, successively acquired volume scans with orthogonal fast scan directions are registered retrospectively in order to estimate and correct eye motion. Registration is performed by optimizing a large scale numerical problem as given by a global objective function using one dense displacement field for each input volume and special regularization based on the time structure of the acquisition process. After optimization, each volume is undistorted and a single merged volume is constructed that has superior signal quality compared to the input volumes. Experiments were performed using 3D OCT data from the macula and optic nerve head acquired with a high-speed ultra-high resolution 850 nm spectral OCT as well as wide field data acquired with a 1050 nm swept source OCT instrument. Evaluation of registration performance and result stability as well as visual inspection shows that the algorithm can correct for motion in all three dimensions and on a per A-scan basis. Corrected volumes do not show visible motion artifacts. In addition, merging multiple motion corrected and registered volumes leads to improved signal quality. These results demonstrate that motion correction and merging improves image quality and should also improve morphometric measurement accuracy from volumetric OCT data.

Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head.

Glaucoma causes variations of the optic nerve head (ONH). We propose an approach to describe the inter subject variability of the ONH by dense deformation fields produced by non-rigid registration. Non-glaucoma but acquisition related variations are eliminated in a preprocessing step. Standard non-rigid registration was extended by a radial smoothing regularizer to preserve the ONH natural circular appearance and to enforce a physiological suitable mapping. The proposed approach captures successfully ONH variations.

Objective Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation. Methods The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification. Results On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p -value of 0.019 emphasizes the statistical difference of both approaches. Conclusions The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos.

Zur frühzeitigen Diagnose von Erkrankungen am Augenhintergrund existieren automatische Bildverarbeitungsmethoden. Für die Effektivität solcher Verfahren und für die ärztliche Bewertung ist die Bildqualität der Eingabedaten entscheidend. Wir stellen einen neuen segmentierungsfreien Algorithmus zur automatischen Qualitätsmessung einzelner Fundusbilder vor, der Clustering-Merkmale, Schärfemaße und globale Bildstatistiken kombiniert. Die Güte der Methode wird mit einem etablierten Verfahren zur Qualitätsmessung von Retina-Fundusbildern verglichen, das nur Clustering-Merkmale auf gefilterten Bildern verwendet. Der neue Algorithmus zeigt dabei auf 302 Fundusfotos mit einer Klassifikationsgenauigkeit von 90,3% eine deutlich höhere Zuverlässigkeit als das Vergleichsverfahren mit 69,5%.

Der Papillenrand ist ein entscheidendes Merkmal zur Erkennung von krankhaften Veränderungen am Augenhintergrund. Zur Auswertung ist eine Segmentierung nötig, die meist manuell durch den Augenarzt vorgenommen werden muss. Eine robuste, automatische Segmentierung der Papille kann den Arzt unterstützen, die Reliabilität der Segmentierung erhöhen und eine Basis für eine automatische Diagnose schaffen. Die vorgestellte Methode optimiert ein Segmentierungsverfahren mittels Ausreißerdetektion und Spline-Interpolation auf radial abgetasteten binarisierten Reflektionsbildern des Heidelberg Retina Tomographen (HRT). Der Vergleich mit bestehenden Verfahren zeigt, dass der Segmentierungsfehler um 9% reduziert werden konnte und das Verfahren stabiler gegen Artefakte ist.

Glaucoma is one of the most common causes for blindness worldwide. Screening is adequate to detect glaucoma at an early stage. Although it is supported by computer assisted tools no further information from former clinical studies is incorporated.We devised a novel visualization tool that presents additional comparative image data for the diagnosis process. Automated computation of a glaucoma risk index on color fundus photographs is used to initially position an undiagnosed image in reference data. The index achieves a competitive glaucoma detection rate. The combination of the automated risk index and the new visualization technique is an important tool towards a faster and more reliable diagnosis of glaucoma.

Glaucoma is a disease of the optic nerve that threatens the eyesight of the patients and is one of the leading causes of blindness. Because healing of died retinal nerve fibers is not possible early detection and prevention is essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We devised a novel, automated glaucoma classification system that does not depend on segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening examinations. It applies a standard pattern recognition pipeline with a 2-stage classification. Our system has an 86% success rate on a data set containing a mixture of 200 real images of healthy and glaucomatous eyes. The performance of the system is comparable to human medical experts in detecting glaucomatous retina fundus images.