Department of Information Systems, Computing and Mathematics Brunel University, London, UK.
Diagnostic Imaging and Radiology, Children's National Medical Center, 24105 Washington, DC USA.
Health Inf Sci Syst. 2014 Jan 27;2:2. doi: 10.1186/2047-2501-2-2. eCollection 2014.
The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.
视网膜血管分析在检测和治疗视网膜疾病方面起着重要作用。在这篇综述中，我们提出了一种自动分割眼底视网膜图像血管的方法。该方法可用于支持现代眼科的非侵入性诊断，以便早期发现视网膜疾病、评估治疗效果或进行临床研究。本研究结合了偏置校正和自适应直方图均衡化来增强血管的外观。然后使用概率建模提取血管，并通过期望最大化算法进行优化。该方法在 STARE 和 DRIVE 数据集的眼底视网膜图像上进行了评估。实验结果与一些最近发表的视网膜血管分割方法进行了比较。实验结果表明，我们的方法在整体性能上表现最佳，与人类专家的表现相当。
