A optical design of confocal scanning laser ophthalmoscope based on Chinese eye model is presented, which is featured by using a Chinese eye model. Firstly, a Chinese eye model, which is obtained by reverse building from Chinese population, is adopted as eye model. Secondly, a famous optical design architecture of confocal scanning laser ophthalmoscope is selected to build our design based on the Chinese eye model. In our design, the illumination light path, the retina imaging light path, and the corneal reflection light path are all implemented. The simulation show that our design has high resolution.
A Hartmann-Shack wavefront sensor is designed for adaptive optics confocal scanning laser ophthalmoscopy. The Hartmann-Shack wavefront sensor designed consists of a lenslet array with square configuration, sub-aperture size 0.2mm×0.2mm , focal length 5mm and a CCD camera with pixel size 3.75μm×3.75μm.Thedynamic range and measurement accuracy of the HSWFS are simulated through the software MATLAB. Theresult of simulation indicates that focus dynamic range±14λ (λ=635nm), wavefront measurement accuracyreach RMS λ/100, all these indicators reached the requirements of the system. Finally, experimentsofcalibration by spherical wavefront were done on these indicators.
Our retinal vessel segmentation approach utilizes deep hierarchical semantic segmentation along with a closing operation. From fundus images, the retinal vessels are extracted using the supervised learning segmentation algorithm. Deep semantic segmentation that provides a hierarchical solution is adopted for rough retinal vessel segmentation. The rough segmentation results are then processed by a closing operation to refine the segmented retinal vessels. We performed experiments and comparisons with ground truths to evaluate the qualitative and quantitative effectiveness of our method. Our method effectively segments retinal vessels, as demonstrated by the experimental results.
KEYWORDS: Angiography, Image resolution, Image filtering, Gaussian filters, Current controlled current source, Signal to noise ratio, Digital filtering, Scientific research, Image quality, Denoising
Although fundus fluorescein angiography is an imaging modality that supports ophthalmic diagnosis, it requires the intravenous injection of harmful fluorescein dye. We propose the synthesis of fluorescein angiography images from fundus structure images to avoid injection. Specifically, we automatically synthesize high-resolution fundus fluorescein angiography images through an algorithm that integrates a generative adversarial networks and image stitching and enhancement. By evaluating the peak signal-to-noise ratio and structural similarity index of the proposed algorithm, pix2pix, and cycleGAN, we confirmed the superior performance of our proposal. To further validate the proposed algorithm, we compared the fundus fluorescein angiography images synthesized by our algorithm, pix2pix, and cycleGAN. The experimental results show that our algorithm provides the highest resolution and quality in the synthesis of fluorescein angiography images from fundus structure images among the evaluated methods.
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