基于均值距离测度的医学图像配准

针对互信息测度在配准医学图像时易陷入局部极值、速度慢的缺点,提出了基于均值不等式的均值距离测度.首先根据均值不等式推导出5种均值距离测度：方根-算术均值距离(SAM)、方根-几何均值距离(SGM)、方根-调和均值距离(SHM)、算术-几何均值距离(AGM)、算术-调和均值距离(AHM).然后通过人体脑部CT/MR和MR-T1/PD图像的刚体配准实验,从函数曲线、配准精度、计算时间和收敛性能方面,对互信息与5种均值距离信息测度进行了比较与分析.实验结果表明,在不损失配准精度的前提下,AHM和SAM测度可以获得更快的配准速度,对噪声有很强的鲁棒性.

Mean Divergence Measures for Medical Image Registration

In order to improve registration speed of mutual information measure, the mean divergence measures are used as the similarity measure of medical image registration. The square root arithmetic mean divergence (SAM), square root geometric mean divergence (SGM), square root harmonic mean divergence (SHM), arithmetic geometric mean divergence (AGM), and arithmetic harmonic mean divergence (AHM) measures are applied to rigid registration of computed tomography (CT)/ magnetic resonance (MR) and MR-T1/PD images. The function curves, registration accuracy, registration time and convergence of these measures are studied in comparison with that of mutual information. The results show that the proposed registration measures have similar function curves and accuracy with mutual information, and the AHM and SAM measures have significant improvements in registration speed.