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dc.contributor.authorGarcía Allende, Pilar Beatriz
dc.contributor.authorConde Portilla, Olga María 
dc.contributor.authorKrishnaswamy, Venkataramanan
dc.contributor.authorHoopes, P. Jack
dc.contributor.authorPogue, Brian William
dc.contributor.authorMirapeix Serrano, Jesús María 
dc.contributor.authorLópez Higuera, José Miguel 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2013-06-27T07:01:36Z
dc.date.available2013-06-27T07:01:36Z
dc.date.issued2010-05-17
dc.identifier.issn1996-756X
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/10902/2525
dc.description.abstractConventional imaging systems used today in surgical settings rely on contrast enhancement based on color and intensity and they are not sensitive to morphology changes at the microscopic level. Elastic light scattering spectroscopy has been shown to distinguish ultra-structural changes in tissue. Therefore, it could provide this intrinsic contrast being enormously useful in guiding complex surgical interventions. Scatter parameters associated with epithelial proliferation, necrosis and fibrosis in pancreatic tumors were previously estimated in a quantitative manner. Subtle variations were encountered across the distinct diagnostic categories. This work proposes an automated methodology to correlate these variations with their corresponding tumor morphologies. A new approach based on the aggregation of the predictions of K-nearest neighbors (kNN) algorithm and Artificial Neural Networks (ANNs) has been developed. The major benefit obtained from the combination of the distinct classifiers is a significant increase in the number of pixel localizations whose corresponding tissue type is reliably assured. Pseudo-color diagnosis images are provided showing a strong correlation with sample segmentations performed by a veterinary pathologist.es_ES
dc.format.extent10 p.es_ES
dc.language.isoenges_ES
dc.publisherSPIE Society of Photo-Optical Instrumentation Engineerses_ES
dc.rights© 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.es_ES
dc.sourceProceedings of SPIE, 2010, vol. 7715, 77151Bes_ES
dc.sourceBiophotonics: Photonic Solutions for Better Health Care II, Bruselas, 2010es_ES
dc.subject.otherAutomatic classificationes_ES
dc.subject.otherTumores_ES
dc.subject.otherNecrosises_ES
dc.subject.otherConfocal reflectance imaginges_ES
dc.subject.otherScatteres_ES
dc.subject.otherFeature extractiones_ES
dc.subject.otherK-nearest neighbors (kNN)es_ES
dc.subject.otherArtificial neural networks (ANN)es_ES
dc.titleAutomated ensemble segmentation of epithelial proliferation, necrosis, and fibrosis using scatter tumor imaginges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.relation.publisherVersionhttp://dx.doi.org/10.1117/12.854559es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1117/12.854559
dc.type.versionpublishedVersiones_ES


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