基于特征的相关光学和电子显微镜图像配准

D. Nam, J. Mantell, Lorna Hodgson, D. Bull, P. Verkade, A. Achim
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引用次数: 6

摘要

在本文中,我们提出了一种基于特征的配准算法,用于大错位的明场光学显微镜图像和透射电子显微镜图像。我们首先使用基于梯度的单次投票算法检测细胞质心。然后通过查找翻转、平移和旋转参数来对齐图像,这将最大化伪细胞中心之间的重叠。通过将其与手动对齐的图像进行比较,我们证明了该方法的有效性。结合配准的光学和电子显微镜图像可以揭示细胞结构的细节与空间和高分辨率的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature-based registration for correlative light and electron microscopy images
In this paper we present a feature-based registration algorithm for largely misaligned bright-field light microscopy images and transmission electron microscopy images. We first detect cell centroids, using a gradient-based single-pass voting algorithm. Images are then aligned by finding the flip, translation and rotation parameters, which maximizes the overlap between pseudo-cell-centers. We demonstrate the effectiveness of our method, by comparing it to manually aligned images. Combining registered light and electron microscopy images together can reveal details about cellular structure with spatial and high-resolution information.
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