Liu Jiangang, Tian Jie
Medical Image Processing Group, Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Science, P.O. Box 2728, Beijing 100080, China.
Int J Biomed Imaging. 2007;2007:93479. doi: 10.1155/2007/93479.
Traditional mutual information (MI) function aligns two multimodality images with intensity information, lacking spatial information, so that it usually presents many local maxima that can lead to inaccurate registration. Our paper proposes an algorithm of adaptive combination of intensity and gradient field mutual information (ACMI). Gradient code maps (GCM) are constructed by coding gradient field information of corresponding original images. The gradient field MI, calculated from GCMs, can provide complementary properties to intensity MI. ACMI combines intensity MI and gradient field MI with a nonlinear weight function, which can automatically adjust the proportion between two types MI in combination to improve registration. Experimental results demonstrate that ACMI outperforms the traditional MI and it is much less sensitive to reduced resolution or overlap of images.
传统的互信息(MI)函数利用强度信息对齐两个多模态图像,缺乏空间信息,因此通常会出现许多局部最大值,这可能导致配准不准确。我们的论文提出了一种强度和梯度场互信息自适应组合(ACMI)算法。通过对相应原始图像的梯度场信息进行编码来构建梯度编码图(GCM)。从GCM计算得到的梯度场互信息可以为强度互信息提供互补特性。ACMI利用非线性权重函数将强度互信息和梯度场互信息相结合,该函数可以自动调整组合中两种互信息之间的比例以改善配准。实验结果表明,ACMI优于传统的互信息,并且对图像分辨率降低或重叠的敏感度要低得多。