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

Paul Richard Barber; Richard James Edens; B. Vojnovic

doi:10.1117/12.590547

23 March 2005 Acquisition and analysis of spectral image data by linear un-mixing, cluster computing and a novel spectral imager

Paul Richard Barber, Richard James Edens, B. Vojnovic

Author Affiliations +

Proceedings Volume 5694, Spectral Imaging: Instrumentation, Applications, and Analysis III; (2005) https://doi.org/10.1117/12.590547
Event: SPIE BiOS, 2005, San Jose, CA, United States

Abstract

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.

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Paul Richard Barber, Richard James Edens, and B. Vojnovic "Acquisition and analysis of spectral image data by linear un-mixing, cluster computing and a novel spectral imager", Proc. SPIE 5694, Spectral Imaging: Instrumentation, Applications, and Analysis III, (23 March 2005); https://doi.org/10.1117/12.590547

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KEYWORDS

Computing systems

Imaging systems

Imaging spectroscopy

Image processing

Tissues

Image analysis

Image segmentation

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