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Please use this identifier to cite or link to this item: http://ri.uaemex.mx/handle20.500.11799/41185
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dc.contributor.authorJENNIFER LYNN REYNOSO MUÑOZen_EU
dc.contributor.authorALMA DELIA CUEVAS RASGADOen_EU
dc.contributor.authorFarid García Lamonten_EU
dc.contributor.authorADOLFO GUZMAN ARENASen_EU
dc.creatorJENNIFER LYNN REYNOSO MUÑOZ-
dc.creatorALMA DELIA CUEVAS RASGADO-
dc.creatorFarid García Lamont-
dc.creatorADOLFO GUZMAN ARENAS-
dc.date2015-08-01-
dc.date.accessioned2019-03-12T23:43:50Z-
dc.date.available2019-03-12T23:43:50Z-
dc.identifierhttp://hdl.handle.net/20.500.11799/41185-
dc.identifier.urihttp://ri.uaemex.mx/handle20.500.11799/41185-
dc.descriptionThis paper focuses on the representation of magnetic resonances of different parts of the human body, such as knees, spinal column, arms, elbows, etc., using ontologies. First, it maps the resonance images in a multimedia database. Then, automatically, using the SIFT pattern recognition algorithm, descriptors of the images stored in the database are extracted in order to recover useful data for the user; it uses the ontologies as an artificial intelligence tool and, in consequence, reduces generation of useless data. Why do we think this is an interesting task? Because, if the user requires information about any topics or (s)he has some illness or needs to undergo magnetic resonance, this tool will show him/her images and text to convey a better understanding, helping to obtain useful conclusions. Artificial intelligence techniques are used, such as machine learning, knowledge representation, and pattern recognition. The ontological relations introduced here are based on the common representation of language, using definition dictionaries, Roget’s thesaurus, synonym dictionaries, and other resources. The system generates an output in the OM ontological language [1]. This language represents a structure where our system adds the data scanned by the SIFT algorithm. The tests have been made in Spanish; however, thanks to the portability of our system, it is possible to extend the method to any language.-
dc.descriptionProyecto UAEM 3454CHT/2013-
dc.languagespa-
dc.publisherIEEE Latin America Transactions-
dc.relation10.1109/TLA.2015.7332153;-
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0-
dc.source1548-0992-
dc.subjectartificial intelligence-
dc.subjectontology-
dc.subjectmultimedia database-
dc.subjectpattern recognition-
dc.subjectmagnetic resonance-
dc.subjectinfo:eu-repo/classification/cti/7-
dc.titleAutomatic mapping magnetic resonance images into multimedia database using SIFT-
dc.typearticle-
dc.audiencestudents-
dc.audienceresearchers-
item.fulltextNo Fulltext-
item.grantfulltextnone-
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