Zhang Jie, Rangarajan Anand
Dept. of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611-6120, USA.
Inf Process Med Imaging. 2005;19:725-37. doi: 10.1007/11505730_60.
We extend an information metric from intermodality (2-image) registration to multimodality (multiple-image) registration so that we can simultaneously register multiple images of different modalities. And we also provide the normalized version of the extensible information metric, which has better performance in high noise situations. Compared to mutual information which can even become negative in the multiple image case, our metric can be easily and naturally extended to multiple images. After using a new technique to efficiently compute high dimensional histograms, the extensible information metric can be efficiently computed even for multiple images. To showcase the new measure, we compare the results of direct multimodality registration using high-dimensional histogramming with repeated intermodality registration. We find that registering 3 images simultaneously with the new metric is more accurate than pair-wise registration on 2D images obtained from synthetic magnetic resonance (MR) proton density (PD), MR T2 and MR T1 3D volumes from Brain Web. We perform the unbiased registration of 5 multimodality images of anatomy, CT, MR PD, T1 and T2 from Visible Human Male Data with the normalized metric and high-dimensional histogramming. Our results demonstrate the efficacy of the metrics and high-dimensional histogramming in affine, multimodality image registration.
我们将一种信息度量从多模态间（双图像）配准扩展到多模态（多图像）配准，以便能够同时对不同模态的多个图像进行配准。并且我们还提供了可扩展信息度量的归一化版本，其在高噪声情况下具有更好的性能。与在多图像情况下甚至可能变为负数的互信息相比，我们的度量可以轻松且自然地扩展到多图像。在使用一种新技术高效计算高维直方图之后，即使对于多图像，也能够高效计算可扩展信息度量。为了展示这种新度量，我们将使用高维直方图法的直接多模态配准结果与重复的多模态间配准结果进行比较。我们发现，使用新度量同时对3幅图像进行配准，比从Brain Web的合成磁共振（MR）质子密度（PD）、MR T2和MR T1三维体数据中获取的二维图像进行逐对配准更为准确。我们使用归一化度量和高维直方图法对来自可视人男性数据的5幅解剖、CT、MR PD、T1和T2多模态图像进行无偏配准。我们的结果证明了这些度量和高维直方图法在仿射多模态图像配准中的有效性。
