基于主成分分析的三维医学图像快速配准算法

A fast 3-D medical image registration algorithm using principal component analysis

本文提出了一种新的基于主成分分析的三维医学图像快速配准算法。传统的基于灰度的方法需要考虑整个三维数据的灰度信息,计算复杂度大,无法满足临床需要。而本算法利用数据的轮廓特征,通过主成分分析计算出图像的质心和主轴,通过对齐质心和主轴完成配准。实验结果表明此方法能准确,快速地处理图像刚性配准问题,特别适用于三维医学图像的配准。

This paper presents a new 3-D image registration method based on the principal component analysis （PCA）. Compared with intensity-based registration methods using the whole volume intensity information, our approach utilizes PCA to estimate the centroid and principal axis, and completes the registration by aligning the centroid and principal axis. We evaluated the effectiveness of this approach by applying it to simulated and actual brain image data （MR, CT, PET, and SPECT）. The experimental results indicate that the algorithm is effective, especially for registration of 3-D medical images.