Ma B, Narayanan R, Park H, Hero A O, Bland P H, Meyer C R
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
Inf Process Med Imaging. 2007;20:270-82. doi: 10.1007/978-3-540-73273-0_23.
The interest in registering a set of images has quickly risen in the field of medical image analysis. Mutual information (MI) based methods are well-established for pairwise registration but their extension to higher dimensions (multiple images) has encountered practical implementation difficulties. We extend the use of alpha mutual information (alphaMI) as the similarity measure to simultaneously register multiple images. alphaMI of a set of images can be directly estimated using entropic graphs spanning feature vectors extracted from the images, which is demonstrated to be practically feasible for joint registration. In this paper we are specifically interested in monitoring malignant tumor changes using simultaneous registration of multiple interval MR or CT scans. Tumor scans are typically a decorrelating sequence due to the cycles of heterogeneous cell death and growth. The accuracy of joint and pairwise registration using entropic graph methods is evaluated by registering several sets of interval exams. We show that for the parameters we investigated simultaneous joint registration method yields lower average registration errors compared to pairwise. Different degrees of decorrelation in the serial scans are studied and registration performance suggests that an appropriate scanning interval can be determined for efficiently monitoring lesion changes. Different levels of observation noise are added to the image sequences and the experimental results show that entropic graph based methods are robust and can be used reliably for multiple image registration.
在医学图像分析领域，对一组图像进行配准的关注度迅速上升。基于互信息（MI）的方法在成对配准方面已经成熟，但将其扩展到更高维度（多幅图像）时遇到了实际实现困难。我们扩展了α互信息（alphaMI）作为相似性度量的应用，以同时对多幅图像进行配准。一组图像的alphaMI可以使用跨越从图像中提取的特征向量的熵图直接估计，这被证明对于联合配准在实际中是可行的。在本文中，我们特别关注通过同时配准多个间隔期的磁共振成像（MR）或计算机断层扫描（CT）来监测恶性肿瘤的变化。由于异质性细胞死亡和生长的循环，肿瘤扫描通常是一个去相关序列。通过对几组间隔期检查进行配准，评估了使用熵图方法进行联合配准和成对配准的准确性。我们表明，对于我们研究的参数，与成对配准相比，同时联合配准方法产生的平均配准误差更低。研究了序列扫描中不同程度的去相关性，配准性能表明可以确定合适的扫描间隔以有效监测病变变化。在图像序列中添加了不同水平的观测噪声，实验结果表明基于熵图的方法具有鲁棒性，可可靠地用于多幅图像配准。
