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Parallelization of classification algorithms for medical imaging on a cluster computing system

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2 Author(s)
Daggett, T. ; Dept. of Comput. Sci. & Eng., Connecticut Univ., Storrs, CT, USA ; Greenshields, I.R.

Examines the SPMD (single program, multiple data) parallel implementation of image classification algorithms on a cluster of personal computers. The small-scale cluster environment employed utilizes two quite different application programming interfaces (APIs) for inter-process communications: message passing and virtual shared memory. We quantitatively compare both of these communication approaches in conjunction with a small-scale cluster for medical image classification by presenting the SPMD parallelization of three well-known context-independent image classification algorithms: nearest mean, maximum likelihood and K nearest neighbors. These classic approaches are applied to massive medical images, and the resulting average speedup using both message-passing and virtual shared memory inter-process communications is presented

Published in:

Computer-Based Medical Systems, 1998. Proceedings. 11th IEEE Symposium on

Date of Conference:

12-14 Jun 1998