The visual depth perception is composed of monocular and binocular depth cues. Studies show that in absence of binocular depth cues the performance of visuomotor tasks like pointing to or grasping objects is limited. Thus, binocular depth cues are of great importance for motor control required in everyday life. However, binocular depth cues like retinal disparity (basis for stereopsis) might be influenced due to developmental disorders of the visual system. For example, amblyopia in which one eye's visual input is not processed leads to loss of stereopsis. The primary amblyopia treatment is occlusion of the healthy eye to force the amblyopic eye to train. However, improvements in stereopsis are poor. Therefore, binocular treatments arose that equilibrate both eyes' visual input to enable binocular vision. However, most approaches rely on divided stimuli which do not account for loss of stereopsis. We created a Virtual Reality (VR) with reduced monocular depth cues in which a stereoscopic task is shown to both eyes simultaneously, consisting of two balls jumping towards the user. One ball appears closer to the user which must be identified. To evaluate the task performance the reaction time is measured. We validated our approach with 18 participants with stereopsis under three contrast settings including one leading to monocular vision. The number of correct responses reduces from 90% under binocular vision to 52% under monocular vision corresponding to random guessing. Our results indicate that it is possible to disable monocular depth cues and create a dynamic stereoscopic task inside a VR.

3D displays enable immersive visual impressions but the impact on the human perception still is not fully understood. Viewing conditions like the convergence-accommodation (C-A) conflict have an unnatural influence on the visual system and might even lead to visual discomfort. As visual perception is individual we assumed the impact of simulated 3D content on the visual system to be as well. In this study we aimed to analyze the stereoscopic visual performance of 17 subjects for disparities inside and outside the in literature defined zone of comfortable viewing to provide an individual evaluation of the impact of increased disparities on the performance of the visual system. Stereoscopic stimuli were presented in a four-alternative forced choice (4AFC) setup in different disparities. The response times as well as the correct decision rates indicated the performance of stereoscopic vision. The results showed that increased disparities lead to a decline in performance. Further, the impact of the presented disparities is dependent on the difficulty of the task. The decline of performance as well as the deciding disparities for the decline were subject dependent.

Fundus imaging is one of the most frequently used modalities for screening, diagnosis of eye diseases and some vascular abnormalities. Due to its wide availability, automatic evaluation of fundus images offers great potential benefits to current clinical practice. The basis of many automatic evaluations or diagnosis is the segmentation of the eye background, most notably, the detection of the optic disk and the segmentation of the retinal vessel tree. In this work we propose a variant of the fast radial symmetry transform (FRST), adapted to its application in the detection of the optic disk in fundus images. We evaluated and compared the performance of our method to the standard FRST and the similar, gradient based circular Hough transform using 45 images of a high resolution database with gold standard information available. We demonstrated in our experiments that the proposed method outperforms the state-of-the-art algorithms with 0.051 ± 0.073 optic disk diameter localization error in average.

Fundus imaging is the most commonly used modality to collect information about the human eye background. Objective and quantitative assessment of quality for the acquired images is essential for manual, computer-aided and fully automatic diagnosis. In this paper, we present a no-reference quality metric to quantify image noise and blur and its application to fundus image quality assessment. The proposed metric takes the vessel tree visible on the retina as guidance to determine an image quality score. In our experiments, the performance of this approach is demonstrated by correlation analysis with the established full-reference metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). We found a Spearman rank correlation for PSNR and SSIM of 0.89 and 0.91. For real data, our metric correlates reasonable to a human observer, indicating high agreement to human visual perception.