Huang Ke, Yan Michelle
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):645-53. doi: 10.1007/11866565_79.
Accurate retinal blood vessel detection offers a great opportunity to predict and detect the stages of various ocular and systemic diseases, such as glaucoma, hypertension and congestive heart failure, since the change in width of blood vessels in retina has been reported as an independent and significant prospective risk factor for such diseases. In large-population studies of disease control and prevention, there exists an overwhelming need for an automatic tool that can reliably and accurately identify and measure retinal vessel diameters. To address requirements in this clinical setting, a vessel detection algorithm is proposed to quantitatively measure the salient properties of retinal vessel and combine the measurements by Bayesian decision to generate a confidence value for each detected vessel segment. The salient properties of vessels provide an alternative approach for retinal vessel detection at a level higher than detection at the pixel level. Experiments show superior detection performance than currently published results using a publicly available data set. More importantly, the proposed algorithm provides the confidence measurement that can be used as an objective criterion to select reliable vessel segments for diameter measurement.
准确的视网膜血管检测为预测和检测各种眼部及全身性疾病（如青光眼、高血压和充血性心力衰竭）的阶段提供了绝佳机会，因为视网膜血管宽度的变化已被报道为这些疾病独立且重要的前瞻性风险因素。在疾病控制与预防的大规模人群研究中，迫切需要一种能够可靠且准确地识别和测量视网膜血管直径的自动工具。为满足这一临床需求，提出了一种血管检测算法，用于定量测量视网膜血管的显著特征，并通过贝叶斯决策合并测量结果，为每个检测到的血管段生成一个置信度值。血管的显著特征为视网膜血管检测提供了一种比像素级检测更高层次的替代方法。实验表明，使用公开数据集时，该算法的检测性能优于当前已发表的结果。更重要的是，所提出的算法提供了置信度测量，可作为选择可靠血管段进行直径测量的客观标准。
