Abstract：
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images.
摘要：
理想情况下,进行眼部疾病筛查，预计将使用专门的医疗设备来捕获视网膜眼底图像。然而,由于这种设备通常价格昂贵且便携性低，随着技术的发展和智能手机的出现，新的便携式和更便宜的筛查选项已经出现，其中一个是D-Eye设备.与专用设备相比，该设备和与智能手机相关的其他类似设备在捕获的视网膜视频中呈现较低的质量和较小的视野，但有足够的质量来进行医学预筛查。如有必要，可以转介个人进行专门筛查以获得医学诊断。提出了两种方法来从这些较低质量的视频（视网膜区）中提取相关区域。第一种是基于经典的图像处理方法，例如阈值和霍夫圆变换。另一个通过应用神经网络来执行视网膜位置的提取，这是文献中报道的具有良好目标检测性能的方法之一，YOLOv4，这被证明是首选的应用方法。从相关的视网膜区域实施镶嵌技术，以获得具有更高视场的更多信息的单个图像。它分为两个阶段：在第一阶段中，应用GLAMpoints神经网络来提取相关点。执行一些单应变换以在相同的参考中具有图像的公共区域的重叠。在第二阶段,在图像之间的过渡中执行平滑处理。
