Institute of Physiology, Academy of Sciences of the Czech Republic, v.v.i., Department of Biomathematics, Vídeňská 1083, CZ-14220 Prague 4, Czech Republic.
Microsc Microanal. 2011 Dec;17(6):923-36. doi: 10.1017/S1431927611011937. Epub 2011 Nov 3.
When biological specimens are cut into physical sections for three-dimensional (3D) imaging by confocal laser scanning microscopy, the slices may get distorted or ruptured. For subsequent 3D reconstruction, images from different physical sections need to be spatially aligned by optimization of a function composed of a data fidelity term evaluating similarity between the reference and target images, and a regularization term enforcing transformation smoothness. A regularization term evaluating the total variation (TV), which enables the registration algorithm to account for discontinuities in slice deformation (ruptures), while enforcing smoothness on continuously deformed regions, was proposed previously. The function with TV regularization was optimized using a graph-cut (GC) based iterative solution. However, GC may generate visible registration artifacts, which impair the 3D reconstruction. We present an alternative, multilabel TV optimization algorithm, which in the examined samples prevents the artifacts produced by GC. The algorithm is slower than GC but can be sped up several times when implemented in a multiprocessor computing environment. For image pairs with uneven brightness distribution, we introduce a reformulation of the TV-based registration, in which intensity-based data terms are replaced by comparison of salient features in the reference and target images quantified by local image entropies.
当生物样本被切成物理切片，通过共聚焦激光扫描显微镜进行三维（3D）成像时，切片可能会变形或破裂。为了进行后续的 3D 重建，需要通过优化由参考图像和目标图像之间相似性的评价数据保真度项和强制转换平滑的正则化项组成的函数来对来自不同物理切片的图像进行空间对齐。之前提出了一个正则化项，用于评价总变差（TV），它使得配准算法能够考虑到切片变形的不连续性（破裂），同时对连续变形区域进行平滑处理。具有 TV 正则化的函数使用基于图割（GC）的迭代解决方案进行了优化。然而，GC 可能会产生明显的配准伪影，从而影响 3D 重建。我们提出了一种替代的多标签 TV 优化算法，该算法可以防止 GC 产生的伪影。该算法比 GC 慢，但在多处理器计算环境中实现时，可以加速数倍。对于亮度分布不均匀的图像对，我们提出了一种基于 TV 的配准的重新表述，其中基于强度的数据项被替换为参考图像和目标图像中显著特征的比较，通过局部图像熵来量化。
