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

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.