Publicación:
Cinética de renderizado y relación de hardware para la digitalización de imágenes del Neurobanco de la Universidad de Antioquia

Vista sencilla de ítem
dc.contributor.authorRueda, Carlos A.spa
dc.contributor.authorJaramillo González, Lauraspa
dc.contributor.authorSoto-Ospina, Alejandrospa
dc.contributor.authorAraque Marín, Pedronelspa
dc.contributor.authorVillegas Lanau, Andrésspa
dc.date.accessioned2020-06-21 00:00:00
dc.date.accessioned2022-06-17T20:20:46Z
dc.date.available2020-06-21 00:00:00
dc.date.available2022-06-17T20:20:46Z
dc.date.issued2020-06-21
dc.description.abstractLos cortes encefálicos en regiones anatómicas específicas, son claves para el entendimiento y descripción de algunas patologías relacionadas con enfermedades neurodegenerativas, el procesamiento de imágenes es un área emergente que permite la digitalización de la información, para la creación de un banco digital a partir de las imágenes de los cortes de encéfalo en la línea de investigación del Neurobanco del Grupo de Neurociencias de Antioquia. El software utilizado para el procesamiento de las imágenes fue Agisoft© Metashape, con el cual se hace el renderizado tridimensional de las fotos, ya que es clave conocer las condiciones de hardware para explorar las potencialidades del render en el software, para un menor tiempo, considerando conceptos de mask_tie point y mask_key point, unidades de procesamiento de cómputo y unidades de procesamiento gráfico. Los conjuntos de software obtenidos, cálculos cinéticos y relación de procesamiento independiente y combinado de gráfico, se determina que el mejor conjunto de hardware desde un aspecto técnico y funcional es un computador de escritorio con la combinación de una unidad de procesamiento Intel-i7 8700 con una tarjeta de video GTX 1060. No obstante, respecto a una relación de economía, el mejor hardware es Intel i5 9400 con una tarjeta de video GTX 1660, dado a que esta combinación da una mayor potencia en el procesamiento de imagen tridimensional, que un hardware con solo procesador, así este sea de alta potencia. Finalmente, como aspecto relevante, se espera complementar el análisis a partir del estudio de un conjunto de hardware de la compañía Radeon, que ofrece alternativas como las tarjetas de video AMD Rx 5700XT.  spa
dc.description.abstractBrain cuts in specific anatomical regions are key to the understanding and description of some pathologies related to neurodegenerative diseases, image processing is an emerging area allows the digitalization of information, for the creation of a digital bank from brain, cuts images in the Neurobanco research line of the Neuroscience Group of Antioquia. The software used for image processing was Agisoft © Metashape, with which the three-dimensional rendering of photos is done since it is essential to know the hardware conditions to explore the potential of rendering in the software, for a shorter time, considering concepts of mask_tie point and mask_key point, computational processing units and graphics processing units. The obtained software sets, kinetic calculations and independent and combined graphics processing ratio, determined that the best hardware set from a technical and functional aspect is a desktop computer with the combination of a high power processing unit with high power a video card (Intel-i7 8700 with a GTX 1060 video card). However, regarding an economic relationship, the best hardware is a medium power processing and a medium or high power graphic card (Intel i5 9400 with a GTX 1660 video card), given that this combination gives greater potential in the three-dimensional image processing than hardware with only one processor, even if it is of high power. Finally, as a relevant aspect, it is expected to complement the analysis from the study of set hardware from the Radeon company, which offers alternatives such as AMD Rx 5700XT video cards.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.24050/reia.v17i34.1405
dc.identifier.eissn2463-0950
dc.identifier.issn1794-1237
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/5113
dc.identifier.urlhttps://doi.org/10.24050/reia.v17i34.1405
dc.language.isospaspa
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.relation.bitstreamhttps://revistas.eia.edu.co/index.php/reveia/article/download/1405/1351
dc.relation.citationeditionNúm. 34 , Año 2020spa
dc.relation.citationendpage11
dc.relation.citationissue34spa
dc.relation.citationstartpage1
dc.relation.citationvolume17spa
dc.relation.ispartofjournalRevista EIAspa
dc.relation.referencesBeltran, R. S. et al. (2018) ‘An evaluation of three-dimensional photogrammetric and morphometric techniques for estimating volume and mass in Weddell seals Leptonychotes weddellii’, PLoS ONE, 13(1), pp. 1–15. doi: 10.1371/journal.pone.0189865.spa
dc.relation.referencesFathi, H. and Brilakis, I. (2011) ‘Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features’, Advanced Engineering Informatics. Elsevier Ltd, 25(4), pp. 760–770. doi: 10.1016/j.aei.2011.06.001.spa
dc.relation.referencesGoodbody, T. R. H., Coops, N. C. and White, J. C. (2019) ‘Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions’, Current Forestry Reports. Current Forestry Reports, pp. 55–75. doi: 10.1007/s40725-019-00087-2.spa
dc.relation.referencesGray, P. C. et al. (2019) ‘Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry’, Methods in Ecology and Evolution, 10(9), pp. 1490–1500. doi: 10.1111/2041-210X.13246.spa
dc.relation.referencesHarris Geospatial Solutions, I. (2020) Collect and Edit Tie Points, L3Harris Geospatial solutions. Available at: https://www.harrisgeospatial.com/docs/CollectingEditingTiePoints.html (Accessed: 11 February 2020).spa
dc.relation.referencesJulien, Fauqueur, Nick , Kingsbury and Ryan, A. (2006) ‘Multiscale keypoint detection using the dual-tree complex wavelet tranform’, IEEE Computer Graphics and Applications. doi: 10.1109/ICIP.2006.312656.spa
dc.relation.referencesLai, J. Y. et al. (2018) ‘A high-resolution texture mapping technique for 3D textured model’, Applied Sciences (Switzerland), 8(11). doi: 10.3390/app8112228.spa
dc.relation.referencesLane, S. N., Richards, K. S. and Chandler, J. H. (1993) ‘Developments in photogrammetry; the geomorphological potential’, Progress in Physical Geography, 17(3), pp. 306–328. doi: 10.1177/030913339301700302.spa
dc.relation.referencesLevine, J. A., Paulsen, R. R. and Zhang, Y. (2012) ‘Mesh processing in medical-image analysis-a Tutorial’, IEEE Computer Graphics and Applications. IEEE, 32(5), pp. 22–28. doi: 10.1109/MCG.2012.91.spa
dc.relation.referencesMikrut, S., Mikrut, Z. (1992) ‘Neural Networks in the Automation of Photogrammetric’, The international archives of the photogrammetry, remote sensing and spatial information sciences, 1988(4), pp. 331–336.spa
dc.relation.referencesNvidia (1995) GEForces-Gtx serie 10, Nvidia Corporation. Available at: https://www.nvidia.com/es-la/geforce/products/ (Accessed: 11 February 2020).spa
dc.relation.referencesR&D work Agisoft LLC (2006) Agisoft-metashape software, Agisoft software solution. Available at: https://www.agisoft.com/ (Accessed: 11 February 2020). Radeon (1969) AMD-Radeon Rx-5700 XT, Silicon valley. Available at: https://www.amd.com/es/corporate/about-amd (Accessed: 11 February 2020).spa
dc.relation.referencesSchenk, T. (2005) Introduction of Photogrammetry, The Ohio State University. Available at: http://www.mat.uc.pt/~gil/downloads/IntroPhoto.pdf (Accessed: 11 February 2020).spa
dc.relation.referencesSong, Z., Mu, X. and Zhou, H. X. (2017) ‘High Performance Computing of Complex Electromagnetic Algorithms Based on GPU/CPU Heterogeneous Platform and Its Applications to em Scattering and Multilayered Medium Structure’, International Journal of Antennas and Propagation, 2017. doi: 10.1155/2017/9173062.spa
dc.relation.referencesSu, Hang, Jampani, Varun, Sun, Deqing, Maji, Subhransu, Kalogerakis, evangelos, Yang, Ming Hsuan, Kautz, J. (2018) ‘SPLATNet : Sparse Lattice Networks for Point Cloud Processing’, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2530–2539. Available at: http://openaccess.thecvf.com/content_cvpr_2018/papers/Su_SPLATNet_Sparse_Lattice_CVPR_2018_paper.pdf.spa
dc.relation.referencesTeodoro, G. et al. (2017) ‘Application Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs: A Case Study with Microscopy Image Analysis’, International Journal of High Performance Computing Applications, pp. 1–39. doi: 10.1177/1094342015594519.Application.spa
dc.relation.referencesUchida, S. (2013) ‘Image processing and recognition for biological images’, Development Growth and Differentiation, 55(4), pp. 523–549. doi: 10.1111/dgd.12054.spa
dc.rightsRevista EIA - 2020spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0spa
dc.sourcehttps://revistas.eia.edu.co/index.php/reveia/article/view/1405spa
dc.subjectRenderizadospa
dc.subjectneurobancospa
dc.subjectdigitalizaciónspa
dc.subjectcortes encefálicosspa
dc.subjectfotogrametría.spa
dc.subjectrenderingeng
dc.subjectneurobankeng
dc.subjectdigitizationeng
dc.subjectbrain cutseng
dc.subjectphotogrammetryeng
dc.titleCinética de renderizado y relación de hardware para la digitalización de imágenes del Neurobanco de la Universidad de Antioquiaspa
dc.title.translatedRendering Kinetics and Hardware Relationship for the Digitization of Images of the Neurobank of the University of Antioquiaeng
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTREFspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dspace.entity.typePublication
Archivos
Colecciones
Revista EIA (Portal de Revistas)