Por favor, use este identificador para citar o enlazar este ítem: http://redi.ufasta.edu.ar:8082/jspui/handle/123456789/1212
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dc.creatorComas, Diego-
dc.creatorMeschino, Gustavo-
dc.creatorCostantino, Sebastián-
dc.creatorPastore, Juan-
dc.creatorCapiel, Carlos-
dc.creatorBallarín, Virginia-
dc.date2016-
dc.date.accessioned2016-11-04T20:05:11Z-
dc.date.accessioned2021-11-26T18:34:35Z-
dc.date.available2016-11-04T20:05:11Z-
dc.date.available2021-11-26T18:34:35Z-
dc.date.issued2016-
dc.identifier.urihttp://redi.ufasta.edu.ar:8082/jspui/handle/123456789/1212-
dc.descriptionFil: Comas, Diego. Universidad FASTA. Facultad de Ingeniería; Argentina.-
dc.descriptionFil: Meschino, Gustavo. Universidad FASTA. Facultad de Ingeniería; Argentina.-
dc.descriptionFil: Costantino, Sebastián. Universidad FASTA. Facultad de Ingeniería; Argentina.-
dc.descriptionFil: Pastore, Juan. Universidad FASTA. Facultad de Ingeniería; Argentina.-
dc.descriptionFil: Capiel, Carlos. Universidad FASTA. Facultad de Ingeniería; Argentina.-
dc.descriptionFil: Ballarín, Virginia. Universidad FASTA. Facultad de Ingeniería; Argentina.-
dc.descriptionThe analysis of structural changes in the brain through magnetic resonance imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with some pathologies, like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. This paper presents a method for segmentation of magnetic resonance images of the brain, based on a classification method using interval type-2 fuzzy logic called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC) which enables computing volumes occupied for the different tissues into the intracranial cavity. In the first stage, a random partition of observations is performed. Data contained in data subsets are analyzed, applying clustering to the observations corresponding to each class in order to discover groups of data with similar properties. Then, interval type-2 membership functions and fuzzy predicates are defined. In the final stage, optimization of parameters regarding the classification system is done. A comparison against various known classification methods was performed. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this goal. Advantages of this approach are presented throughout this paper.-
dc.formatapplication/pdfes_ES
dc.languagespaes_ES
dc.publisherUniversidad FASTA. Facultad de Ingeniería-
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/deed.es_ARes_ES
dc.sourceinstname:Universidad FASTAes_ES
dc.sourcereponame:REDIes_ES
dc.subjectInformática y Saludes_ES
dc.titleMagnetic resonance volumetric techniques: a new segmentation method based on interval type-2 fuzzy logic and clinical applicationses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.typeinfo:ar-repo/semantics/artículoes_ES
dc.typeinfo:eu-repo/semantics/publishedVersiones_ES
Aparece en las colecciones: Facultad de Ingeniería - G.I - Informática y Salud

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