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This article is part of the supplement: 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics

Open Access Research

High performance computing environment for multidimensional image analysis

A Ravishankar Rao1*, Guillermo A Cecchi1 and Marcelo Magnasco2

Author affiliations

1 IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA

2 Rockefeller University, 1230 York Avenue, New York, NY 10021, USA

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Citation and License

BMC Cell Biology 2007, 8(Suppl 1):S9  doi:10.1186/1471-2121-8-S1-S9

Published: 10 July 2007

Abstract

Background

The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications.

Results

We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478× speedup.

Conclusion

Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets.