The adaptive bases algorithm for intensity-based nonrigid image registration
The adaptive bases algorithm for intensity-based nonrigid image registration
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DOI：
10.1109/tmi.2003.819299
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发表时间：
2003-11-01
影响因子：
10.6
通讯作者：
Dawant, BM
中科院分区：
文献类型：
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作者：
Rohde, GK;Aldroubi, A;Dawant, BM
医学图像的非刚性配准对于许多应用都很重要，例如创建总体平均值、基于图集的分割或功能磁共振成像 (fMRI) 图像的几何校正等。近年来，人们提出了许多方法来解决这个问题，其中一类涉及在规则样条网格上最大化基于互信息（MI）的目标函数。这种方法产生了良好的结果，但其计算复杂性与配准图像中最小结构所需的变换的合规性成正比。在这里，我们提出了一种允许变换顺应性的空间适应的方法。这种空间适应使我们能够减少整体变换中的自由度，从而加快该过程并提高其收敛性能。为了开发这种方法，我们引入了几个新颖之处：1）我们依赖径向对称基函数而不是传统上用于模拟变形场的 B 样条； 2）我们提出了一个指标来识别注册不良且需要改进转换的区域； 3）我们将全局配准问题划分为几个较小的问题； 4）我们引入了一种新的约束方案，使我们能够产生拓扑正确的变换。我们将我们提出的方法与更传统的方法进行比较，并表明我们的新算法优于当前使用的算法。
Nonrigid registration of medical images is important for a number of applications such as the creation of population averages, atlas-based segmentation, or geometric correction of functional magnetic resonance imaging (fMRI) images to name a few. In recent years, a number of methods have been proposed to solve this problem, one class of which involves maximizing a mutual information (MI)-based objective function over a regular grid of splines. This approach has produced good results but its computational complexity is proportional to the compliance of the transformation required to register the smallest structures in the image. Here, we propose a method that permits the spatial adaptation of the transformation's compliance. This spatial adaptation allows us to reduce the number of degrees of freedom in the overall transformation, thus speeding up the process and improving its convergence properties. To develop this method, we introduce several novelties: 1) we rely on radially symmetric basis functions rather than B-splines traditionally used to model the deformation field; 2) we propose a metric to identify regions that are poorly registered and over which the transformation needs to be improved; 3) we partition the global registration problem into several smaller ones; and 4) we introduce a new constraint scheme that allows us to produce transformations that are topologically correct. We compare the approach we propose to more traditional ones and show that our new algorithm compares favorably to those in current use.