Retinal vascular disease has always been the focus of medical attention. However, segmentation of the retinal vessels from fundus images is still an open problem due to intensity inhomogeneity in the image and thickness diversity of the retinal vessels. In this paper, we propose Frangi based multi-scale level sets to segment retinal vessels from fundus images. Vascular structures are first enhanced by the Frangi filter with local optimal scales being obtained at the same time. The enhanced image and local optimal scales are taken considered as inputs of the proposed level set models. Effectiveness of the proposed multi-scale level sets to their scale fixed versions has been evaluated using DRIVE and STARE image repositories. In addition, the proposed level set models have been tested on the DRIVE and STARE images. Experiments show that the proposed models produce segmentation accuracy at the same level with state-of-the-art methods.

视网膜血管疾病一直是医学关注的焦点。然而，由于图像中的强度不均匀性和视网膜血管的厚度多样性，从眼底图像分割视网膜血管仍然是一个未解决的问题。在本文中，我们提出基于Frangi的多尺度水平集从眼底图像中分割视网膜血管。首先通过Frangi过滤器增强血管结构，同时获得局部最佳尺度。增强的图像和局部最佳比例被视为建议的水平集模型的输入。已使用DRIVE和STARE图像存储库评估了建议的多尺度级别集对其尺度固定版本的有效性。此外，建议的水平集模型已经在DRIVE和STARE图像上进行了测试。