基于YOLO算法的眼底图像视盘定位方法

视盘的各个参数是衡量眼底健康状况和病灶的重要指标,视盘的检测和定位对于观察视盘的形态尤为重要。在以往的视盘定位研究中,主要根据视盘的形状、亮度、眼底血管的走向等特征使用图像处理的方法对眼底图像中视盘进行定位。由于人为因素影响较大,特征提取时间较长,且视盘定位效率低,因此提出一种基于YOLO算法的眼底图像视盘定位方法。利用YOLO算法将眼底图像划分为N×N的格子,每个格子负责检测视盘中心点是否落入该格子中,通过多尺度的方式和残差层融合低级特征对视盘进行定位,得到不同大小的边界框,最后通过非极大抑制的方式筛选出得分最高的边界框。通过在3个公开的眼底图像数据集(DRIVE、DRISHTI-GS1和MESSIDOR)上,对所提出的视盘定位方法进行测试,定位准确率均为100%,实验同时定位出视盘的中心点坐标,与标准中心点的平均欧氏距离分别为22.36 px、2.52 px、21.42 px,验证了该方法的准确性和通用性。

An optic disc positioning method in fundus images based on YOLO

The parameters of the optic disc are important indicators for measuring the health status and lesions of the fundus. The detection and localization of the optic disc is especially important for observing the shape of the optic disc. Research on optic disc positioning in the past mainly depends on the shape and brightness of the optic disc, and the direction of the fundus blood vessels, and image-processing methods are used to locate the optic disc in fundus images. Due to the influence of human factors, the feature extraction time is long and the optic disc positioning efficiency is low. We propose a method of locating optic disc of the fundus image based on the YOLO algorithm. The YOLO algorithm is used to divide the fundus image into N×N grids, and each grid is responsible for detecting whether the center point of the disc falls into the grid. The multi-scale method and the residual layer are fused with low-level features to locate the disc, and bounding boxes of different sizes are obtained. The bounding box with the highest score is finally selected through non-maximum suppression. We test the proposed localization method on three open databases of fundus images (DRIVE、DRISHTI-GS1 and MESSIDOR). The positioning accuracy is 100%, and the center point coordinates of the optic disc are located in the experiment. The average Euclidean distances to the center points are 22.36 px, 2.52 px, 21.42 px, respectively, which verifies the accuracy and versatility of the method.