首页> 外文会议>2015 Opto-Electronics and Communications Conference >Time stretch imaging with optical data compression for label-free biological cell classification
【24h】

Time stretch imaging with optical data compression for label-free biological cell classification

机译:具有光学数据压缩的时间拉伸成像技术,用于无标记生物细胞分类

获取原文
获取原文并翻译 | 示例

摘要

Real-time instruments that acquire large data sets are needed for detection and classification of outliers. A new class of high throughput real-time instruments based on the photonic time-stretch has led to the discovery of optical rogue waves [1], detection of rare cancer cells [2], and the highest analog-to-digital conversion performance ever achieved [3]. One example of these instruments is the time stretch camera, an imaging modality that features continuous operation at about 100 million frames per second and shutter speed of less than a nanosecond. As an imaging flow-through microscope, the technology is in clinical testing for blood screening. While highly useful for collecting large data sets, the instrument's ultrahigh throughput also creates a big data problem. The system produces a large volume of data in a short time equivalent to several 4K movies per second. Such a data fire hose places a burden on data acquisition, storage, and processing operations and calls for technologies that compress images in optical domain and in real-time. An example of this, based on warped stretch transformation and non-uniform Fourier domain sampling has recently been reported [4]. The paper will provide an overview of the time-stretch microscope with real-time optical image compression, and application of this technology in classification of cancer cell lines in blood.
机译:检测和分类异常值需要获取大量数据的实时仪器。基于光子时间拉伸的一类新型的高通量实时仪器已导致发现光无赖波[1],检测稀有癌细胞[2]和有史以来最高的模数转换性能实现[3]。这些仪器的一个例子是时间拉伸相机,它是一种成像模式,具有每秒约1亿帧的连续操作能力和不到1纳秒的快门速度。作为成像流通式显微镜,该技术正在用于血液筛查的临床测试中。仪器的超高通量虽然对于收集大数据集非常有用,但也会造成大数据问题。该系统在短时间内产生大量数据,相当于每秒几张4K电影。这样的数据传输软管给数据采集,存储和处理操作带来负担,并要求在光域中实时压缩图像的技术。最近有一个基于翘曲拉伸变换和非均匀傅立叶域采样的例子[4]。本文将概述具有实时光学图像压缩的时间拉伸显微镜,以及该技术在血液中癌细胞系分类中的应用。

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