Authors: Salvatore Cuomo; Ardelio Galletti; Livia Marcellino
Addresses: Department of Mathematics and Applications, University of Naples Federico II, Via Cinthia, 80126 Naples, Italy ' Department of Science and Technology, University of Naples 'Parthenope', Centro Direzionale, Isola C4, 80143 Naples, Italy ' Department of Science and Technology, University of Naples 'Parthenope', Centro Direzionale, Isola C4, 80143 Naples, Italy
Abstract: Recently, advanced computing systems are widely adopted in order to intensively elaborate a huge amount of biomedical data in the e-health field. An interesting challenge is to perform real-time diagnosis by means of complex computational environments. In this paper, we suggest to deal the most computationally expensive processing steps of a distributed cloud e-health system by the use of graphics processing units (GPUs). In the case study of the magnetic resonance imaging (MRI), for improving the quality of denoising and helping the real-time diagnosis, we have implemented a GPU parallel algorithm based on the optimised blockwise non-local means (OB-NLM) method. Experimental results have shown a significant improvement of healthcare processing practice in terms of execution time.
Keywords: cloud systems; e-health; MRI denoising; non-local means; NLMs; GPU computing.
International Journal of High Performance Computing and Networking, 2018 Vol.11 No.4, pp.304 - 311
Available online: 05 Jul 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article