Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.
J Digit Imaging. 2013 Apr;26(2):248-58. doi: 10.1007/s10278-012-9501-7.
In this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI's) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI's are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI's. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.
在这项研究中，我们提出了一种使用仿射矩不变量（AMI）的精确视网膜图像配准方法，AMI 是形状描述符。首先，在参考图像和感知图像中提取一些封闭边界区域。然后，计算每个区域的 AMI。选择距离最小的三对点对的质心作为控制点。通过 AMI 的距离测量来执行区域匹配。通过比较参考图像和感知图像中这三个点对构建的三个三角形的角度来评估区域匹配。可以使用这三个控制点对计算仿射变换的参数。该算法应用于有效的 DRIVE 数据库。一般来说（对于每种感知图像都是通过以不同的角度、比例因子和平移因子旋转、缩放和平移参考图像生成的情况），成功率和准确率分别为 95%和 96%。
