SIproc: an open-source biomedical data processing platform for large hyperspectral images
SIproc: an open-source biomedical data processing platform for large hyperspectral images
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DOI：
10.1039/c6an02082h
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发表时间：
2017-04-21
期刊：
影响因子：
4.2
通讯作者：
Mayerich, David
中科院分区：
文献类型：
--
作者：
Berisha, Sebastian;Chang, Shengyuan;Mayerich, David
最近有显着的兴趣在振动光谱社区应用定量光谱成像技术的组织学和临床诊断。然而，所提出的方法中的许多方法需要收集具有与对应的组织学图像相似的区域大小和分辨率的光谱图像。由于光谱图像比传统组织学包含更多的光谱样本，因此产生的数据集的大小可以接近数百千兆字节到兆兆字节。这使得它们很难存储和处理，研究人员处理大型光谱数据集的工具也很有限。基本数学工具（例如MATLAB、Octave和SciPy）非常强大，但需要将数据存储在快速内存中。这种存储器限制对于即使是中等大小的组织学图像也变得不切实际，所述组织学图像的大小可以是数百千兆字节。在本文中，我们提出了一个开源的工具包，旨在执行超光谱图像的核外处理。通过利用图形处理单元（GPU）计算与自适应数据流相结合的优势，我们的软件消除了常见的工作站内存限制，同时实现了比现有应用程序更好的性能。
There has recently been significant interest within the vibrational spectroscopy community to apply quantitative spectroscopic imaging techniques to histology and clinical diagnosis. However, many of the proposed methods require collecting spectroscopic images that have a similar region size and resolution to the corresponding histological images. Since spectroscopic images contain significantly more spectral samples than traditional histology, the resulting data sets can approach hundreds of gigabytes to terabytes in size. This makes them difficult to store and process, and the tools available to researchers for handling large spectroscopic data sets are limited. Fundamental mathematical tools, such as MATLAB, Octave, and SciPy, are extremely powerful but require that the data be stored in fast memory. This memory limitation becomes impractical for even modestly sized histological images, which can be hundreds of gigabytes in size. In this paper, we propose an open-source toolkit designed to perform out-of-core processing of hyperspectral images. By taking advantage of graphical processing unit (GPU) computing combined with adaptive data streaming, our software alleviates common workstation memory limitations while achieving better performance than existing applications.