A cluster computer system for the analysis and classification of massively large biomedical image data.

Autor: Daggett T; T. L. Booth Research Center, Department of Computer Science and Engineering, University of Connecticut, Storrs, USA., Greenshields IR
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 1998 Jan; Vol. 28 (1), pp. 47-60.
DOI: 10.1016/s0010-4825(97)00032-2
Abstrakt: The current trend in medical image acquisition is towards the generation of image datasets which are massively large, either because they exhibit fine x, y, or z resolution, are volumetric, are multispectral, or a combination of all of the preceding. Such images pose a significant computational challenge in their analysis, not only in terms of data throughput, but also in terms of platform costs and simplicity. In this paper we describe the role of a cluster of workstations together with two quite different application programming interfaces (APIs) in the quantitative analysis of anatomic image data from the visible human project using an MRF-Gibbs classification algorithm. We describe the typical architecture of a cluster computer, two API options and the parallelization of the MRF-Gibbs procedure for the cluster. Finally, we show speedup results obtained on the cluster and sample classifications of visible human data.
Databáze: MEDLINE
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