一种双通道辅助的原子力显微镜图像拼接方法
A Bi-channel Aided Stitching of Atomic Force Microscopy Images
AI总结：
针对AFM图像特征稀疏、现有工具难以处理全部变换的问题，提出利用振幅通道辅助形貌通道特征匹配与定位的双通道拼接方法，并以x轴微分实现无振幅图像时的泛化。
摘要：
显微成像是科学研究中的重要工具，能够实现微纳尺度分辨率下的结构可视化。然而，显微成像领域常受到视场（FOV）限制，导致单次采集所能成像的样本范围有限。为克服这一限制，研究人员开发了图像拼接技术，将多张具有重叠区域的图像无缝合并为一幅高分辨率合成图像。从显微镜采集的图像必须经过最优拼接，才能在后续分析中提取准确的物理信息。但是，现有拼接工具在显微图像特征稀疏时往往难以完成图像拼接，或者无法处理图像的全部变换。针对这些问题，我们提出了一种双通道辅助的基于特征的图像拼接方法，并在AFM生成的生物膜图像上展示了其应用。AFM数据的形貌通道图像可捕捉样本的形态细节，而研究人员需要获得拼接后的形貌图像。我们利用AFM数据的振幅通道来最大化匹配特征，并估计原始形貌图像的位置；结果表明，所提出的双通道辅助拼接方法优于传统拼接方法。此外，我们发现沿x轴对形貌图像进行微分可提供与振幅通道图像相似的特征信息，从而在振幅图像不可用时使该方法具备泛化能力。本文展示了该方法在AFM上的应用，但类似方法也可用于具有明场和荧光通道的光学显微成像。我们相信，所提出的工作流程将帮助实验人员避免因错误拼接而导致的错误分析和发现。
英文摘要：
Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on AFM generated biofilm images. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude channel of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional stitching approach. Furthermore, we found that the differentiation of the topographical images along the x-axis provides similar feature information to the amplitude channel image, which generalizes our approach when the amplitude images are not available. Here we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
