在利用颅骨轮廓几何特征配准基础上,提出配准前颅部图像的感兴趣区(ROI)圈定,并在Clifford代数框架下给出一种全新的相对不变量构造方法.该方法以颅部ROI的轮廓数据作为配准点云集,根据颅骨刚体轮廓相似性特点,运用Clifford代数构造相对几何不变量的数学模型及计算模型,并计算配准几何运算需求的平移量和旋转算子,采用3D医学图像的相似性测度直接进行三维数据的配准.数据源及评估使用BrainWeb数据库和美国Vanderbilt大学的"回顾性图像配准评估"项目数据.实验表明,新方法在颅部的ROI区域进行配准,能够精确的定位组织器官的三维位置,执行效率高,配准均值误差在2~4 mm内,达到亚像素级配准精度.%A region of interest (ROI) is delineated based on the registration by using the geometric features of skull contour, and a new construction method of relative invariants put forward under the framework of Clifford algebra. The method proposed regards the contour data of ROI in the skull as the point cloud for registration, and constructs the mathematical and calculation models of the relative geometric invariants according to the similarity of skull contour. After calculating the translation and the rotation operator required for the registration algorithm, the registration of three-dimensional data can be preceded directly by adopting the new similarity measure of the 3D medical image. The registration data are from the BrainWeb database and "Retrospective image registration evaluation" project data of the University of Vanderbilt in the United States. Experiments show that our algorithm has high efficiency in the registration of ROI in the skull. And it can calculate the 3D position of the tissue organ more accurately. The mean error is within 2-4 mm, and the registration accuracy is up to sub-pixel level.
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