Li Ying, Gregori Giovanni, Lam Byron L, Rosenfeld Philip J
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida 33136, USA.
Opt Express. 2011 Dec 19;19(27):26239-48. doi: 10.1364/OE.19.026239.
This paper proposes an automatic algorithm for the montage of OCT data sets, which produces a composite 3D OCT image over a large field of view out of several separate, partially overlapping OCT data sets. First the OCT fundus images (OFIs) are registered, using blood vessel ridges as the feature of interest and a two step iterative procedure to minimize the distance between all matching point pairs over the set of OFIs. Then the OCT data sets are merged to form a full 3D montage using cross-correlation. The algorithm was tested using an imaging protocol consisting of 8 OCT images for each eye, overlapping to cover a total retinal region of approximately 50x35 degrees. The results for 3 normal eyes and 3 eyes with retinal degeneration are analyzed, showing registration errors of 1.5±0.3 and 2.0±0.8 pixels respectively.
本文提出了一种用于OCT数据集拼接的自动算法，该算法可从几个单独的、部分重叠的OCT数据集中生成一个大视野的复合3D OCT图像。首先，以血管嵴作为感兴趣特征，并采用两步迭代程序来最小化OCT眼底图像（OFI）集合中所有匹配点对之间的距离，从而对OFI进行配准。然后，使用互相关将OCT数据集合并以形成完整的3D拼接图像。该算法通过一种成像协议进行测试，该协议为每只眼睛采集8张OCT图像，这些图像相互重叠以覆盖大约50×35度的整个视网膜区域。分析了3只正常眼睛和3只患有视网膜变性眼睛的结果，结果显示配准误差分别为1.5±0.3像素和2.0±0.8像素。
