Adjeroh Donald A, Kandaswamy Umasankar, Odom J Vernon
Lane Department of Computer Science and Electrical Engineering, Vido and Image Processing Laboratory, West Virginia University, Morgantown 26506, USA.
J Opt Soc Am A Opt Image Sci Vis. 2007 May;24(5):1384-93. doi: 10.1364/josaa.24.001384.
With improvements in fundus imaging technology and the increasing use of digital images in screening and diagnosis, the issue of automated analysis of retinal images is gaining more serious attention. We consider the problem of retinal vessel segmentation, a key issue in automated analysis of digital fundus images. We propose a texture-based vessel segmentation algorithm based on the notion of textons. Using a weak statistical learning approach, we construct textons for retinal vasculature by designing filters that are specifically tuned to the structural and photometric properties of retinal vessels. We evaluate the performance of the proposed approach using a standard database of retinal images. On the DRIVE data set, the proposed method produced an average performance of 0.9568 specificity at 0.7346 sensitivity. This compares well with the best-published results on the data set 0.9773 specificity at 0.7194 sensitivity [Proc. SPIE5370, 648 (2004)].
随着眼底成像技术的进步以及数字图像在筛查和诊断中的使用日益增加，视网膜图像的自动分析问题正受到越来越多的关注。我们考虑视网膜血管分割问题，这是数字眼底图像自动分析中的一个关键问题。我们基于纹理基元的概念提出了一种基于纹理的血管分割算法。使用弱统计学习方法，通过设计专门针对视网膜血管的结构和光度特性进行调整的滤波器，为视网膜血管系统构建纹理基元。我们使用标准的视网膜图像数据库评估所提出方法的性能。在DRIVE数据集上，所提出的方法在灵敏度为0.7346时产生了平均特异性为0.9568的性能。这与该数据集上已发表的最佳结果（灵敏度为0.7194时特异性为0.9773 [Proc. SPIE5370, 648 (2004)]）相比具有优势。
