【24h】

Acquisition and analysis of spectral image data by linear un-mixing, cluster computing and a novel spectral imager

机译:通过线性解混,聚类计算和新型光谱成像仪来采集和分析光谱图像数据

获取原文
获取原文并翻译 | 示例

摘要

We describe how spectral imaging, linear un-mixing and cluster computing have been combined to aid biomedical researchers and allow the spatial segmentation and quantitative analysis of immunohistochemically stained tissue section images. A novel cost-effective spectral imager, with a bandwidth of 15 nm between 400 and 700 nm, allows us to record both spatial and spectral data from absorptive and fluorescent chemical probes. The linear un-mixing of this data separates the stain distributions revealing areas of co-localisation and extracts quantitative values of optical density. This has been achieved at the single-pixel level of an image by non-negative least squares fitting. This process can be computationally expensive but great processing speed increases have been achieved through the use of cluster computing. We describe how several personal computers, running Microsoft WindowsXP, can be used in parallel, linked by the MPI (Message Passing Interface) standard. We describe how the free MPICH libraries have been incorporated into our spectral imaging application under the C language and how this has been extended to support features of MPI2 via the commercial WMPI Ⅱ libraries. A cluster of 8 processors, in 4 dual-Athlon-2600+ computers, offered a speed up of a factor of 5 compared to a singleton. This includes the time required to transfer the data throughout the cluster and reflects a processing efficiency of 0.62 (a Cluster Efficacy of 3.0). The cluster was based on a 1000Base-T Ethernet network and appears to be scalable efficiently beyond 8 processors.
机译:我们描述了如何将光谱成像,线性解混和聚类计算相结合以帮助生物医学研究人员,并允许对免疫组织化学染色的组织切片图像进行空间分割和定量分析。一种新颖的具有成本效益的光谱成像仪,在400至700 nm之间具有15 nm的带宽,使我们能够记录来自吸收性和荧光化学探针的空间和光谱数据。该数据的线性解混分离了染色分布,揭示了共定位区域,并提取了光密度的定量值。这是通过非负最小二乘拟合在图像的单像素级别实现的。此过程可能在计算上很昂贵,但是通过使用群集计算已实现了极大的处理速度提高。我们描述了如何通过MPI(消息传递接口)标准链接多台运行Microsoft WindowsXP的个人计算机。我们将描述如何使用C语言将免费的MPICH库合并到我们的光谱成像应用程序中,以及如何通过商业WMPIⅡ库将其扩展为支持MPI2的功能。在4台双Athlon-2600 +计算机中,由8个处理器组成的集群将速度提高了5倍。这包括在整个群集中传输数据所需的时间,并反映出0.62的处理效率(群集效率为3.0)。该集群基于一个1000Base-T以太网,并且似乎可以有效扩展到超过8个处理器。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

联系方式:18141920177 (微信同号)

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