Fast processing of medical images using a new parallel architecture, the Hybrid System

Leo, C.S.; Schröder, Heiko

doi:10.1109/iai.2002.999908

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…General parallelization methods can be found in the literature [6,2]. Parallel image rendering techniques studied by researchers are only capable of exploiting limited parallelism [9,7].…”

Section: Related Workmentioning

confidence: 99%

Acceleration of maximum likelihood estimation for tomosynthesis mammography

Zhang

Meleis

Kaeli

et al. 2006

12th International Conference on Parallel and Distributed Systems - (ICPADS'06)

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Maximum likelihood (ML) estimation is used during tomosynthesis mammography reconstruction. A single reconstruction involves the processing of highresolution projection images, which is both computeintensive and time-consuming. This workload is presently a bottleneck in the accurate diagnosis of breast cancer during screening. This paper presents our parallelization work on an ML algorithm using three different partitioning models: no inter-communication, overlap with inter-communication and non-overlap model. These models are evaluated to obtain the best reconstruction performance given a range of computing environments with different computational power and network speed. Our test results show that the non-overlap method outperforms the other two methods on all five computing platforms evaluated. This parallelization of ML has enabled tomosynthesis to become a viable technology in the breast screening clinic, reducing reconstruction time from 3 hours on a PentiumIV workstation to 6 minutes on a 32-node PentiumIV cluster.

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“…General parallelization methods can be found in the literature [6,2]. Parallel image rendering techniques studied by researchers are only capable of exploiting limited parallelism [9,7].…”

Section: Related Workmentioning

confidence: 99%