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Nonrigid registration of CLSM images of physical sections with discontinuous deformations

机译:具有不连续变形的物理部分的CLSM图像的非刚性配准

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摘要

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.
机译:当通过共聚焦激光扫描显微镜将生物标本切成用于3维(3D)成像的物理切片时,切片可能会变形或破裂。对于后续的3D重建,需要通过优化函数来对来自不同物理部分的图像进行空间对齐,该函数由评估参考图像和目标图像之间相似性的数据保真度项和执行变换平滑度的正则项组成。先前提出了一种评估总变化量(TV)的正则化项,它可以使配准算法考虑到切片变形(断裂)中的不连续性,同时在连续变形的区域上增强平滑度。使用基于图割(GC)的迭代解决方案优化了电视正则化功能。但是,GC可能会生成可见的配准伪像,从而损害3D重建。我们提出了一种替代的多标签电视优化算法,该算法在检查的样本中可以防止GC产生的伪影。该算法比GC慢,但是在多处理器计算环境中实现时可以加快数倍。对于具有不均匀亮度分布的图像对,我们引入了基于电视的配准的重新制定,其中,通过比较参考图像和目标图像中由局部图像熵量化的显着特征,替换了基于强度的数据项。

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