Li Ying, Gregori Giovanni, Knighton Robert W, Lujan Brandon J, Rosenfeld Philip J
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida 33136, USA.
Opt Express. 2011 Jan 3;19(1):7-16. doi: 10.1364/OE.19.000007.
This paper proposes an algorithm to register OCT fundus images (OFIs) with color fundus photographs (CFPs). This makes it possible to correlate retinal features across the different imaging modalities. Blood vessel ridges are taken as features for registration. A specially defined distance, incorporating information of normal direction of blood vessel ridge pixels, is designed to calculate the distance between each pair of pixels to be matched in the pair image. Based on this distance a similarity function between the pair image is defined. Brute force search is used for a coarse registration and then an Iterative Closest Point (ICP) algorithm for a more accurate registration. The registration algorithm was tested on a sample set containing images of both normal eyes and eyes with pathologies. Three transformation models (similarity, affine and quadratic models) were tested on all image pairs respectively. The experimental results showed that the registration algorithm worked well. The average root mean square errors for the affine model are 31 µm (normal) and 59 µm (eyes with disease). The proposed algorithm can be easily adapted to registration for other modality retinal images.
本文提出了一种将光学相干断层扫描眼底图像（OFI）与彩色眼底照片（CFP）进行配准的算法。这使得跨不同成像模态关联视网膜特征成为可能。血管嵴被用作配准特征。设计了一种特别定义的距离，该距离纳入了血管嵴像素法线方向的信息，用于计算成对图像中每对要匹配像素之间的距离。基于此距离定义了成对图像之间的相似性函数。采用暴力搜索进行粗配准，然后使用迭代最近点（ICP）算法进行更精确的配准。该配准算法在一个包含正常眼睛和患病眼睛图像的样本集上进行了测试。分别在所有图像对上测试了三种变换模型（相似性模型、仿射模型和二次模型）。实验结果表明该配准算法效果良好。仿射模型的平均均方根误差在正常眼睛中为31 µm，在患病眼睛中为59 µm。所提出的算法可以很容易地适用于其他模态视网膜图像的配准。
