title: A cluster computer system for the analysis and classification of massively large biomedical image data.
abstract: 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.
authors.affiliation: T. L. Booth Research Center, Department of Computer Science and Engineering, University of Connecticut, Storrs, USA.
authors.forename: T
authors.initials: T
authors.lastname: Daggett
authors.forename: I R
authors.initials: IR
authors.lastname: Greenshields
mesh_terms: D000465:Algorithms; D019412:Anatomy, Cross-Sectional; D003199:Computer Systems; D003625:Data Collection / Q000295:instrumentation; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D008838:Microcomputers; D012984:Software; D014584:User-Computer Interface