Mean Shift迭代构造拓扑保持图像变换

Topology Preserving Image Transformation through Mean Shift Iteration

在图像弹性配准中,基于径向基函数的小形变变换模型存在拓扑关系不能保持的问题.为此针对小形变模型提出一种基于Mean Shift迭代的拓扑保持图像变换方法.首先在拓扑不能保持的区域确定新增控制点对,通过Mean Shift迭代算法调整新增目标控制点的位置,再根据形变曲面的拓扑保持情况和配准度量的改善情况筛选新增控制点对,最后将新增控制点对添加到原始控制点集合中,得到拓扑保持的、配准精度得到提高的图像变换结果.人工图像和实际图像的配准实验结果表明了该方法的有效性.

Transformations based on radial basis functions expansion are not invertible and topology is not preserved in elastic image registration with large deformation.Under the precondition of correct correspondences of control points,a novel topology-preserving transformation is presented in this paper which warps images for large deformation using radial basis functions and Mean Shift iterations.At first,additional points were tacked at topology non-preserving regions where Jacobian values are negative.Mean Shift iteration was adopted to modify additional target points.The optimal additional point pairs,which resulted in positive Jacobian values of transformation function and improved registration accuracy,were selected.They were combined with original control point set to construct topology preserving transformation functions with radial basis functions.The image registration accuracy was improved using the extended control point set.We compared our method with traditional ones and demonstrated that the proposed method was advantageous on preserving topology of deformation field.Experiments of artificial images and real medical images showed the feasibility of our method.