Wang Hao, Zhu Wenhui, Qin Jiayou, Li Xin, Dumitrascu Oana, Chen Xiwen, Qiu Peijie, Razi Abolfazl, Wang Yalin
School of Computing, Clemson University.
School of Computing and Augmented Intelligence, Arizona State University.
IEEE EMBS Int Conf Biomed Health Inform. 2024 Nov;2024. doi: 10.1109/bhi62660.2024.10913865.
Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for efficient annotation. To mitigate these issues, this paper proposes a novel method for detecting retinal branching angles using a self-configured image processing technique. Additionally, we offer an open-source annotation tool and a benchmark dataset comprising 40 images annotated with retinal branching angles. Our methodology for retinal branching angle detection and calculation is detailed, followed by a benchmark analysis comparing our method with previous approaches. The results indicate that our method is robust under various conditions with high accuracy and efficiency, which offers a valuable instrument for ophthalmic research and clinical applications. The dataset and source codes are available at https://github.com/Retinal-Research/RBAD.
检测视网膜图像分析，特别是分支点的几何特征，在眼部疾病诊断中起着至关重要的作用。然而，用于此目的的现有方法通常是粗粒度的，缺乏用于高效标注的细粒度分析。为了缓解这些问题，本文提出了一种使用自配置图像处理技术检测视网膜分支角度的新方法。此外，我们提供了一个开源标注工具和一个包含40张标注有视网膜分支角度的图像的基准数据集。我们详细介绍了视网膜分支角度检测和计算的方法，随后进行了将我们的方法与先前方法进行比较的基准分析。结果表明，我们的方法在各种条件下都具有鲁棒性，具有高精度和高效率，为眼科研究和临床应用提供了一个有价值的工具。数据集和源代码可在https://github.com/Retinal-Research/RBAD获取。
