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Automated techniques for blood vessels segmentation through fundus retinal images: A review

机译：通过眼底视网膜血管分割的自动化技术：综述

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摘要

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.
机译：视网膜是人眼的内部部分，在视力中具有重要作用。眼底摄像机捕获的数字图像非常有用，可分析视网膜的视网膜异常。为了通过眼底图像获取血管信息，需要一种精确和准确的血管分割图像。该分段血管图像最有益于检测视网膜疾病。许多自动化技术广泛用于视网膜血管分段，这是用于视网膜疾病的计算机化诊断系统的主要元素。在存在病变和异常情况下，自动血管分割可能导致更具挑战性的任务。本文简要介绍了各种公开的视网膜图像数据库和各种机器学习技术。本领域展示了研究人员提出了几种基于监督和监督技术的血管分割方法，并主要在公开数据集上评估其结果，例如数字视网膜图像，用于视网膜的血管提取和结构化分析。介绍了对现有的监督和无监督的船舶分割技术或算法的全面审查，描述了每种算法的哲学。本综述对未来研究中的读者有用。

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