基于主相位一致性的医学图像配准

Medical Image Registration Based on Principal Phase Congruency

本文提出了一种新的图像特征——主相位一致性(Principal Phase Congruency,PPC),并在此基础上构造了一种新的基于主相位一致性的配准算法.首先计算不同尺度、方向上的相位一致性,然后利用主成分分析将它们进行融合,从而得到信息更加丰富的主相位一致性;将待配准图像的主相位一致性看作模糊集合,引入模糊数学中的贴近度概念,计算它们的模糊相似性.我们对模拟和真实数据进行了实验,结果表明在图像空间分辨率较低,有噪声影响等情况下该算法具有精度高、鲁棒性强的特点,特别适合于医学图像的配准.

An image attribute,principal phase congruency（PPC）is defined and used to register medical images. Phase congruency is computed on different scale and orientation.Principal phase congruency can be developed from a fusion of the phase congruency by using principal component analysis. A fuzzy similarity measure is chosen as the registration function. We evaluate the effectiveness of the proposed approach by applying it to the simulated and real brain image data. Experimental results indicate that the ftmction is less sensitive to low sampling resolution and noise,do not contain incorrect global maxima that are sometimes found in the traditional algorithm.