Segmentation of the human eye retinal image is an essential step for proper examination and diagnosis by the ophthalmologists or eye care specialists. A technique for vessel segmentation of retinal images is proposed. Retinal images are mostly low‐light images, which are first processed for enhancement of light as well as for detail amplification. Illumination of low‐light images is enhanced, and details are amplified using content‐adaptive filters. For extraction of vessels from retinal images, after low‐light and detail enhancement, the B‐cosfire filter is modified by including extraction of details and small elements, which may otherwise be ignored. A modified B‐cosfire filter is used to extract vessels while minimizing false edges and halo artifacts. The morphological opening is performed to crop vessels that are falsely segmented. The technique is contrasted with other existing methods in terms of accuracy using publicly available datasets. The proposed technique is tested on STARE, CHASE‐DB1, and DRIVE databases. The outcome of the proposed procedure has better accuracy, preserved edges, minimum noise, and artifacts than the state‐of‐the‐art techniques.

中文翻译：

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视网膜图像的血管分割技术

人眼视网膜图像的分割是由眼科医生或眼保健专家进行适当检查和诊断的重要步骤。提出了一种用于视网膜图像的血管分割的技术。视网膜图像大部分是弱光图像，首先对其进行处理，以增强光线并进行细节放大。弱光图像的照明得到增强，并使用内容自适应滤镜放大细节。为了从视网膜图像中提取血管，在弱光和细节增强后，B-cosfire滤镜通过包含细节和小元素的提取进行了修改，否则可以忽略。改进的B-cosfire过滤器用于提取血管，同时最大程度减少假边缘和光晕伪影。对错误分割的农作物进行形态学开放。使用公开可用的数据集，在准确性方面与其他现有方法形成对比。所提出的技术已在STARE，CHASE-DB1和DRIVE数据库上进行了测试。与最新技术相比，所提出程序的结果具有更好的准确性，保留的边缘，最小的噪声和伪影。