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

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. (C) 1998 Elsevier Science Ltd. All rights reserved.