提出了一种基于最优Atlas图像搜索和局部加权B样条变换的全自动非刚性分层配准分割感兴趣区域(ROI)方法。实验结果表明,所提算法配准的ROI准确度达到95.6%,归一化互信息值为1.8432,均方根误差为1.12%,相关系数提高了18.33%。相比其他配准方法,所提方案的配准精度及准确度明显提升,对临床辅助诊断有重要意义。

A fully automatic and non-rigid hierarchical registration separation ROI (Region of Interest) method based on optimal Atlas image search and local weighted B spline transform is proposed. The experimental results show that the accuracy of the registration of the proposed algorithm is 95.6%, the normalized mutual information value is 1.8432, the root mean square error is 1.12%, and the correlation coefficient is increased by 18.33%. Compared with other registration methods, the registration accuracy and precision of this registered method have obviously improved, which is of great significance for clinical assistant diagnosis.