• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Optical diagnostics and sensing XVIII: Toward Point-of-Care Diagnostics >Optimized computational imaging methods for small-target sensing in lens-free holographic microscopy
【24h】

Optimized computational imaging methods for small-target sensing in lens-free holographic microscopy

机译：无透镜全息显微镜中用于小目标感测的优化计算成像方法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Lens-free holographic microscopy is a promising diagnostic approach because it is cost-effective, compact, and suitable for point-of-care applications, while providing high resolution together with an ultra-large field-of-view. It has been applied to biomedical sensing, where larger targets like eukaryotic cells, bacteria, or viruses can be directly imaged without labels, and smaller targets like proteins or DNA strands can be detected via scattering labels like micro- or nano-spheres. Automated image processing routines can count objects and infer target concentrations. In these sensing applications, sensitivity and specificity are critically affected by image resolution and signal-to-noise ratio (SNR). Pixel super-resolution approaches have been shown to boost resolution and SNR by synthesizing a high-resolution image from multiple, partially redundant, low-resolution images. However, there are several computational methods that can be used to synthesize the high-resolution image, and previously, it has been unclear which methods work best for the particular case of small-particle sensing. Here, we quantify the SNR achieved in small-particle sensing using regularized gradient-descent optimization method, where the regularization is based on cardinal-neighbor differences, Bayer-pattern noise reduction, or sparsity in the image. In particular, we find that gradient-descent with sparsity-based regularization works best for small-particle sensing. These computational approaches were evaluated on images acquired using a lens-free microscope that we assembled from an off-the-shelf LED array and color image sensor. Compared to other lens-free imaging systems, our hardware integration, calibration, and sample preparation are particularly simple. We believe our results will help to enable the best performance in lens-free holographic sensing.
机译：无透镜全息显微技术是一种有前途的诊断方法，因为它具有成本效益，结构紧凑且适用于即时护理应用，同时提供高分辨率和超大视野。它已应用于生物医学传感，其中较大的目标（如真核细胞，细菌或病毒）可以直接成像而无需标记，而较小的目标（如蛋白质或DNA链）可以通过散射标记（如微球或纳米球）进行检测。自动化的图像处理程序可以对物体进行计数并推断出目标浓度。在这些传感应用中，灵敏度和特异性受到图像分辨率和信噪比（SNR）的严重影响。像素超分辨率方法已显示出可以通过从多个部分冗余的低分辨率图像中合成高分辨率图像来提高分辨率和SNR。但是，有几种计算方法可用于合成高分辨率图像，并且以前，尚不清楚哪种方法最适合小颗粒感测的特定情况。在这里，我们使用正则化梯度下降优化方法对小颗粒感测中实现的SNR进行量化，其中正则化基于基数-邻居差异，Bayer模式降噪或图像中的稀疏性。特别是，我们发现基于稀疏正则化的梯度下降最适合小颗粒感测。这些计算方法是对使用无透镜显微镜采集的图像进行评估的，该透镜是由现成的LED阵列和彩色图像传感器组装而成的。与其他无透镜成像系统相比，我们的硬件集成，校准和样品制备特别简单。我们相信我们的结果将有助于实现无透镜全息感测的最佳性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号