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.
Mouse is one of the most common animal models used in retinal researching, and obtaining its fundus images in vivo have significant significance for finding out formation and development mechanism of retinopathy, resolution and field-of-view are the key in fundus imaging. In this paper, we present an ophthalmoscope based on line scanning confocal technology, which utilizes one-dimension scanning line beam to increase image resolution and frame rate, it could capture mouse fundus at 1730*1730 μm field of view and 24 fps frame rate, retinal capillaries and vessels could be distinguished through reflectance and fluorescent images.
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