• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Imaging, manipulation, and analysis of biomolecules, cells, and tissues XV >Comparing Methods for Analysis of Biomedical Hyperspectral Image Data
【24h】

Comparing Methods for Analysis of Biomedical Hyperspectral Image Data

机译：生物医学高光谱图像数据分析的比较方法

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

Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.
机译：在过去的20年中，高光谱成像技术已经适应了生物医学成像领域对分子特异性鉴定的需求。应用范围从单细胞显微镜到整个动物体内成像，从基础研究到临床系统。为了实现这一增长，需要更快，更有效的高光谱滤波技术和更灵敏的检测器。因此，生物医学高光谱成像的增长潜力很高，并且许多高光谱成像选项已经可以商业获得。然而，尽管用于生物医学成像的高光谱技术不断发展，但很少有工作可以帮助高光谱成像仪器的用户选择合适的分析算法。在这里，我们提出了一种通过将实验图像数据与理论“假设情景”相结合来比较光谱分析算法有效性的方法。这种方法使我们能够量化表征高光谱成像研究的几个关键结果：灵敏度线性，阳性检出斜率，动态范围和假阳性事件。我们提供了使用这种方法的结果，用于比较几种常见的光谱分析算法在强组织自发荧光中检测弱荧光蛋白发射的有效性。适用于非常广泛的应用，允许对生物学，硬件和计算分析相结合的有效性进行定量评估，以检测特定的分子标记。

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