In conventional fundus photography, illuminating light is delivered to the interior of the eye through the pupil. To avoid reflection from cornea and crystalline lens, peripheral area of the pupil is used for delivering illumination light and only the central part of the pupil can be used for collecting imaging light. Therefore, the optical design of conventional fundus cameras is sophisticated, the field of view is limited, and pupil dilation is required for evaluating the retinal periphery which is frequently affected by diabetic retinopathy (DR), retinopathy of premature (ROP), and other chorioretinal conditions. Trans-scleral illumination has been proposed as one alternative illumination method to achieve wide field fundus examination not requiring pharmacologic pupil dilation. However, clinical deployment of trans-scleral illumination failed due to the contact mode illumination and imaging, and complication of instrument operation. Here we report a nonmydriatic wide field fundus camera employing trans-pars-planar illumination which delivers illuminating light through the pars plana, an area outside of the pupil without contacting the eye. Trans-pars-planar illumination frees the entire pupil for imaging purpose only, and thus wide field fundus photography can be readily achieved with less pupil dilation. For proof-of-concept testing, using all off-the-shelf components a prototype instrument that can achieve 90° fundus view coverage in single-shot fundus images, without the need of pharmacologic pupil dilation was demonstrated.
It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).
High resolution is important for sensitive detection of subtle distortions of retinal morphology at an early stage of eye diseases. We demonstrate virtually structured detection (VSD) as a feasible method to achieve in vivo super-resolution ophthalmoscopy. A line-scanning strategy was employed to achieve a super-resolution imaging speed up to 127 frames/s with a frame size of 512×512 pixels. The proof-of-concept experiment was performed on anesthetized frogs. VSD-based super-resolution images reveal individual photoreceptors and nerve fiber bundles unambiguously. Both image contrast and signal-to-noise ratio are significantly improved due to the VSD implementation.