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SIproc: an open-source biomedical data processing platform for large hyperspectral images

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Abstract

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.

Graphical abstract: SIproc: an open-source biomedical data processing platform for large hyperspectral images

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Publication details

The article was received on 17 Sep 2016, accepted on 14 Nov 2016, published on 23 Nov 2016 and first published online on 23 Nov 2016


Article type: Paper
DOI: 10.1039/C6AN02082H
Citation: Analyst, 2017, Advance Article
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    SIproc: an open-source biomedical data processing platform for large hyperspectral images

    S. Berisha, S. Chang, S. Saki, D. Daeinejad, Z. He, R. Mankar and D. Mayerich, Analyst, 2017, Advance Article , DOI: 10.1039/C6AN02082H

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