College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 1H9, Canada.
Sci Rep. 2022 Jul 13;12(1):11868. doi: 10.1038/s41598-022-16089-3.
Automated fundus screening is becoming a significant programme of telemedicine in ophthalmology. Instant quality evaluation of uploaded retinal images could decrease unreliable diagnosis. In this work, we propose fractal dimension of retinal vasculature as an easy, effective and explainable indicator of retinal image quality. The pipeline of our approach is as follows: utilize image pre-processing technique to standardize input retinal images from possibly different sources to a uniform style; then, an improved deep learning empowered vessel segmentation model is employed to extract retinal vessels from the pre-processed images; finally, a box counting module is used to measure the fractal dimension of segmented vessel images. A small fractal threshold (could be a value between 1.45 and 1.50) indicates insufficient image quality. Our approach has been validated on 30,644 images from four public database.
自动眼底筛查正成为眼科远程医疗的一个重要项目。即时上传视网膜图像的质量评估可以减少不可靠的诊断。在这项工作中，我们提出视网膜血管的分形维数作为视网膜图像质量的一种简单、有效和可解释的指标。我们方法的流程如下：利用图像预处理技术将来自可能不同来源的输入视网膜图像标准化为统一的样式；然后，使用改进的深度学习赋能的血管分割模型从预处理图像中提取视网膜血管；最后，使用盒子计数模块测量分割血管图像的分形维数。较小的分形阈值（可能在 1.45 到 1.50 之间）表示图像质量不足。我们的方法已经在来自四个公共数据库的 30644 张图像上进行了验证。
