Li Kaccie Y, Roorda Austin
School of Optometry, University of California, Berkeley, CA 94720, USA.
J Opt Soc Am A Opt Image Sci Vis. 2007 May;24(5):1358-63. doi: 10.1364/josaa.24.001358.
In making noninvasive measurements of the human cone mosaic, the task of labeling each individual cone is unavoidable. Manual labeling is a time-consuming process, setting the motivation for the development of an automated method. An automated algorithm for labeling cones in adaptive optics (AO) retinal images is implemented and tested on real data. The optical fiber properties of cones aided the design of the algorithm. Out of 2153 manually labeled cones from six different images, the automated method correctly identified 94.1% of them. The agreement between the automated and the manual labeling methods varied from 92.7% to 96.2% across the six images. Results between the two methods disagreed for 1.2% to 9.1% of the cones. Voronoi analysis of large montages of AO retinal images confirmed the general hexagonal-packing structure of retinal cones as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the reliability and practicality of having an automated solution to this problem.
在进行人眼视锥细胞镶嵌的无创测量时，标记每个单独的视锥细胞是不可避免的任务。手动标记是一个耗时的过程，这推动了自动化方法的开发。实现了一种用于在自适应光学（AO）视网膜图像中标注视锥细胞的自动化算法，并在真实数据上进行了测试。视锥细胞的光学纤维特性有助于算法的设计。在来自六张不同图像的2153个手动标记的视锥细胞中，自动化方法正确识别出其中的94.1%。在这六张图像中，自动化标记方法与手动标记方法之间的一致性在92.7%至96.2%之间变化。两种方法对1.2%至9.1%的视锥细胞的结果不一致。对AO视网膜图像的大拼接图进行的Voronoi分析证实了视网膜视锥细胞的一般六边形排列结构以及视网膜各部分视锥细胞密度的一般变异性。我们测量结果的一致性证明了针对此问题采用自动化解决方案的可靠性和实用性。
