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arXiv 1607.04404physics.opticsq-bio.TO
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离轴数字全息显微术的自动傅里叶空间区域识别滤波

Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy

Xuefei He, Chuong Vinh Nguyen, Mrinalini Pratap, Yujie Zheng, Yi Wang, David R. Nisbet, Richard J Williams, Melanie Rug, Alexander G. Maier, Woei Ming Lee

摘要:

生物样品的自动化无标记定量成像可以极大地有利于高通量疾病诊断。数字全息显微镜(DHM)是一种功能强大的定量无标记成像工具,可非侵入性地检索细胞样品的结构细节。在离轴DHM中,傅立叶空间中合适的空间滤波窗口对重建相位图像的质量至关重要。在这里,我们描述了一个区域识别方法,结合形状识别与迭代阈值提取频率分量的最佳形状。区域识别技术提供了全自动的自适应滤波,可以在各种样本和成像条件下工作。当通过光学散射生物水凝胶基质成像时,该技术超越了以前的直方图阈值技术,而无需任何手动干预。最后,我们自动提取疟原虫感染和未感染的红细胞之间的光学高度的统计差异。本文所述的方法为在厚细胞培养物中对活细胞成像的自动DHM成像中实现更大的自主性铺平了道路。

英文摘要:

Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here pave way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures.

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。

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