Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/63426
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dc.contributor.authorMartinez, Beatrizen_US
dc.contributor.authorLeon, Raquelen_US
dc.contributor.authorFabelo Gómez, Himar Antonioen_US
dc.contributor.authorOrtega, Samuelen_US
dc.contributor.authorPiñeiro, Juan F.en_US
dc.contributor.authorZbigniew Szolna,Adamen_US
dc.contributor.authorHernandez, Mariaen_US
dc.contributor.authorEspino, Carlosen_US
dc.contributor.authorO’shanahan, Aruma J.en_US
dc.contributor.authorCarrera, Daviden_US
dc.contributor.authorBisshopp, Saraen_US
dc.contributor.authorSosa, Coraliaen_US
dc.contributor.authorMarquez, Marianoen_US
dc.contributor.authorCamacho, Rafaelen_US
dc.contributor.authorde la Luz Plaza, Mariaen_US
dc.contributor.authorMorera, Jesusen_US
dc.contributor.authorMarrero Callicó, Gustavo Ivánen_US
dc.date.accessioned2020-01-22T10:49:37Z-
dc.date.available2020-01-22T10:49:37Z-
dc.date.issued2019en_US
dc.identifier.issn1424-8220en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/63426-
dc.description.abstractHyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.en_US
dc.languageengen_US
dc.relationIdentificación Hiperespectral de Tumores Cerebrales (Ithaca)en_US
dc.relationPlataforma H2/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturalesen_US
dc.relationHyperspectral Imaging Cancer Detection (HELICoiD)en_US
dc.relation.ispartofSensorsen_US
dc.sourceSensors [1424-8220], v. 19 (24), artículo 5481en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherBrain canceren_US
dc.subject.otherHyperspectral imagingen_US
dc.subject.otherIntraoperative imagingen_US
dc.subject.otherFeature selectionen_US
dc.subject.otherImage-guided surgeryen_US
dc.subject.otherGenetic algorithmen_US
dc.subject.otherParticle swarm optimizationen_US
dc.subject.otherAnt colony optimizationen_US
dc.subject.otherSupport vector machineen_US
dc.subject.otherMachine learningen_US
dc.titleMost relevant spectral bands identification for brain cancer detection using hyperspectral imagingen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/s19245481en_US
dc.identifier.pmid19-
dc.identifier.scopus85076707257-
dc.identifier.isi000517961400144-
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dc.contributor.authorscopusid57212453744-
dc.contributor.authorscopusid57212456639-
dc.contributor.authorscopusid56405568500-
dc.contributor.authorscopusid57189334144-
dc.contributor.authorscopusid57189323824-
dc.contributor.authorscopusid14032568700-
dc.contributor.authorscopusid8616779200-
dc.contributor.authorscopusid57208489676-
dc.contributor.authorscopusid57200532309-
dc.contributor.authorscopusid55809751300-
dc.contributor.authorscopusid57200531623-
dc.contributor.authorscopusid57200524989-
dc.contributor.authorscopusid57208493219-
dc.contributor.authorscopusid57213808968-
dc.contributor.authorscopusid57208573686-
dc.contributor.authorscopusid35466252100-
dc.contributor.authorscopusid56006321500-
dc.identifier.issue24-
dc.description.firstpage5481en_US
dc.relation.volume19en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid34693328-
dc.contributor.daisngid12737463-
dc.contributor.daisngid34770098-
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dc.contributor.daisngid35033594-
dc.description.numberofpages28en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Martinez, B-
dc.contributor.wosstandardWOS:Leon, R-
dc.contributor.wosstandardWOS:Fabelo, H-
dc.contributor.wosstandardWOS:Ortega, S-
dc.contributor.wosstandardWOS:Pineiro, JF-
dc.contributor.wosstandardWOS:Szolna, A-
dc.contributor.wosstandardWOS:Hernandez, M-
dc.contributor.wosstandardWOS:Espino, C-
dc.contributor.wosstandardWOS:O'Shanahan, AJ-
dc.contributor.wosstandardWOS:Carrera, D-
dc.contributor.wosstandardWOS:Bisshopp, S-
dc.contributor.wosstandardWOS:Sosa, C-
dc.contributor.wosstandardWOS:Marquez, M-
dc.contributor.wosstandardWOS:Camacho, R-
dc.contributor.wosstandardWOS:Plaza, MD-
dc.contributor.wosstandardWOS:Morera, J-
dc.contributor.wosstandardWOS:Callico, GM-
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INGen_US
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptIUMA Sistemas de Información y Comunicaciones-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.orcid0000-0002-9794-490X-
crisitem.author.orcid0000-0002-7519-954X-
crisitem.author.orcid0000-0002-3784-5504-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.fullNameFabelo Gómez, Himar Antonio-
crisitem.author.fullNameOrtega Sarmiento, Samuel-
crisitem.author.fullNameZbigniew Szolna,Adam-
crisitem.author.fullNameMarrero Callicó, Gustavo Iván-
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