A Majorization-Minimization Algorithm for Neuroimage Registration.
A Majorization-Minimization Algorithm for Neuroimage Registration.
复制标题
神经图像配准的专业化最小化算法。
DOI：
10.1137/22m1516907
复制
发表时间：
2024
影响因子：
2.1
通讯作者：
Lange,Kenneth
中科院分区：
文献类型：
--
作者：
Zhou,Gaiting;Tward,Daniel;Lange,Kenneth
基于强度的图像配准对于神经成像任务是至关重要的，例如3D重建、时间序列对准和公共坐标映射。通常用于解决这个问题的基于梯度的优化方法需要仔细选择步长。这种限制带来了大量的时间和计算成本。在这里，我们提出了一个梯度无关的刚体运动配准算法的基础上优化最小化（MM）原则。我们的基于强度的MM算法的每一次迭代都简化为一个简单的点集刚性配准问题，具有完全避免步长问题的封闭形式的解决方案。详细的算法，并推导出其更实用的截断形式的误差界。MM算法的性能被证明是更有效的比梯度下降模拟图像和尼氏染色的小鼠大脑冠状切片。我们还比较了MM算法和另一种无梯度配准算法块匹配法之间的异同。最后，该算法更复杂的问题进行了讨论。
Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.