caa a22 4500 445851724 CHVBK 20180317145415.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s11227-011-0597-1 doi (NATIONALLICENCE)springer-10.1007/s11227-011-0597-1 A GPU-based implementation of the MRF algorithm in ITK package [Elektronische Daten] [Pedro Valero, José Sánchez, Diego Cazorla, Enrique Arias] The analysis of medical image, in particular Magnetic Resonance Imaging (MRI), is a very useful tool to help the neurologists on the diagnosis. One of the stages on the analysis of MRI is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in the literature several packages to carry out this analysis, and of course, the classification tasks. One of them is the Insight Segmentation and Registration Toolkit (ITK). The analysis of MRI is an expensive computational task. In order to reduce the execution time spent on the analysis of MRI, parallelism techniques can be used. Currently, Graphics Processing Units (GPUs) are becoming a good choice to reduce the execution time of several applications at a low cost. In this paper, the authors present a GPU-based classification using MRF from the sequential implementation that appears in the ITK package. The experimental results show a spectacular execution time reduction being the GPU-based implementation up to 118 times faster than the sequential implementation included in the ITK package. Moreover, this result is also observed by reducing the total power consumption in a significant amount. Springer Science+Business Media, LLC, 2011 Magnetic resonance imaging nationallicence Markov random fields nationallicence Insight toolkit nationallicence Graphics processing units nationallicence Valero Pedro Albacete Research Institute of Informatics, Avda. España s/n, 02071, Albacete, Spain aut Sánchez José Computing Systems Dept., University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain aut Cazorla Diego Computing Systems Dept., University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain aut Arias Enrique Computing Systems Dept., University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain aut The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/3(2011-12-01), 403-410 0920-8542 58:3<403 2011 58 11227 https://doi.org/10.1007/s11227-011-0597-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11227-011-0597-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Valero Pedro Albacete Research Institute of Informatics, Avda. España s/n, 02071, Albacete, Spain aut NATIONALLICENCE 700 1- Sánchez José Computing Systems Dept., University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain aut NATIONALLICENCE 700 1- Cazorla Diego Computing Systems Dept., University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain aut NATIONALLICENCE 700 1- Arias Enrique Computing Systems Dept., University of Castilla-La Mancha, Avda. España s/n, 02071, Albacete, Spain aut NATIONALLICENCE 773 0- The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/3(2011-12-01), 403-410 0920-8542 58:3<403 2011 58 11227 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer