Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA.
Neuroimage. 2010 May 1;50(4):1485-96. doi: 10.1016/j.neuroimage.2010.01.040. Epub 2010 Jan 22.
Groupwise registration has been recently introduced to simultaneously register a group of images by avoiding the selection of a particular template. To achieve this, several methods have been proposed to take advantage of information-theoretic entropy measures based on image intensity. However, simplistic utilization of voxelwise image intensity is not sufficient to establish reliable correspondences, since it lacks important contextual information. Therefore, we explore the notion of attribute vector as the voxel signature, instead of image intensity, to guide the correspondence detection in groupwise registration. In particular, for each voxel, the attribute vector is computed from its multi-scale neighborhoods, in order to capture the geometric information at different scales. The probability density function (PDF) of each element in the attribute vector is then estimated from the local neighborhood, providing a statistical summary of the underlying anatomical structure in that local pattern. Eventually, with the help of Jensen-Shannon (JS) divergence, a group of subjects can be aligned simultaneously by minimizing the sum of JS divergences across the image domain and all attributes. We have employed our groupwise registration algorithm on both real (NIREP NA0 data set) and simulated data (12 pairs of normal control and simulated atrophic data set). The experimental results demonstrate that our method yields better registration accuracy, compared with a popular groupwise registration method.
组间配准最近被引入，以通过避免选择特定模板来同时注册一组图像。为此，已经提出了几种方法来利用基于图像强度的信息论熵度量来实现这一点。然而，简单地利用体素级图像强度不足以建立可靠的对应关系，因为它缺乏重要的上下文信息。因此，我们探索了属性向量的概念作为体素特征，而不是图像强度，以指导组间配准中的对应检测。具体来说，对于每个体素，属性向量是从其多尺度邻域计算得到的，以捕获不同尺度的几何信息。然后，从局部邻域估计属性向量中每个元素的概率密度函数 (PDF)，从而提供该局部模式下潜在解剖结构的统计摘要。最终，借助 Jensen-Shannon (JS) 散度，可以通过最小化图像域和所有属性的 JS 散度之和来同时对齐一组对象。我们已经在真实（NIREP NA0 数据集）和模拟数据（12 对正常对照和模拟萎缩数据集）上使用了我们的组间配准算法。实验结果表明，与流行的组间配准方法相比，我们的方法具有更好的配准精度。
